JURNAL ILMIAH MERPATI VOL. 10, NO. 3 DECEMBER 2022

p-ISSN: 2252-3006

e-ISSN: 2685-2411

Comparison of Kernel Support Vector Machine in Predicting Judges' Decisions at the Bekasi District Court

Harry Dwiyana Kartikaa1, Getah Ester Hayatulahb2, Ali Khumaidia3 aFaculty of Engineering, Universitas Krisnadwipayana, Indonesia bFaculty of Law, Universitas Krisnadwipayana, Indonesia

e-mail: 1[email protected], 2[email protected], 3[email protected]

Abstrak

Proses persidangan suatu perkara pidana di Pengadilan Negeri Bekasi tahun 20192021 rata-rata lama proses untuk memutuskan perkara oleh hakim adalah 65-an hari. Pada penelitian ini mengusulkan penggunaan machine learning sebagai alat bantu untuk mempercepat keputusan hakim. Data penelitian yang digunakan adalah jenis acara pidana biasa dengan status perkara minutasi yang dipublikasikan sebanyak 1.642 kasus. Proses pengolahan data mengunakan python dengan preprocessing data case folding, remove punctuation, tokenization dan removal stopword kemudian untuk pembobotan kata menggunakan TF-IDF. Untuk memprediksi putusan lama pemidanaan menggunakan pendekatan klasifikasi Support Vector Machine. Hasil perbandingan pemodelan klasifikasi menggunakan SVM dengan 4 kernel yaitu linear (89,4%), RBF (88,4%), sigmoid (88,4%), dan polynomial (89,1%). Kernel SVM terbaik adalah kernel linear dengan nilai akurasi sebesar 89,4% dan nilai error sebesar 10,6%.

Kata kunci: Kernel, Pengadilan Bekasi, Prediksi, Putusan Hakim, SVM

Abstract

The trial process of a criminal case at the Bekasi District Court in 2019-2021 the average length of the process for deciding cases by judges is 65 days. This study proposes the use of machine learning as a tool to speed up judge decisions. The research data used is the type of ordinary criminal procedure with the status of published cases of 1,642 minutations. The data processing uses python with preprocessing case folding data, remove punctuation, tokenization and stopword removal then for word weighting using TF-IDF. To predict the longterm sentencing decision using the Support Vector Machine classification approach. The results of the comparison of classification modeling using SVM with 4 kernels are linear (89.4%), RBF (88.4%), sigmoid (88.4%), and polynomial (89.1%). The best SVM kernel is a linear kernel with an accuracy value of 89.4% and an error value of 10.6%.

Keywords : Bekasi Court, Judge's Decision, Kernels, Prediction, SVM

automation is not something new in the legal field. In the 1990s there was Lexis-Nexis and

Westlaw which made it easier to find data. Several technologies that support other areas of law have also been developed. In forensic linguistics using text classification, detection of a

person's personality, gender identity and age [2]. Doctrinal research methods have been used

for a long time, which include the interpretation of laws and practical problem solving, over time have developed towards a systematic, innovative with simplification [3]. The systematic

exposition of rules on doctrinal law develops with the ability to analyze the relationship of probability, difficulty and prediction of the future [4]. With the documentation of the court decision process that can be accessed by the public and the development of machine learning, several studies that facilitate understanding cases using a quantitative approach are applied in understanding traditional law.[5], [6].

In the trial process of a criminal case at the Bekasi District Court in 2019-2021, the average length of the process required to decide a case by a judge is 65 days. The use of machine learning and technology can help ease the work of judges in deciding cases. One solution to the problem that is currently being developed is to use artificial intelligence technology. The ability of artificial intelligence related to human language processing, especially in court claims used in this study is the natural language processing (NLP) method [7]. NLP is a method that examines the interaction between humans and computers using human language [8]. The hope of this research is that it can assist judges in determining decisions so that case handling can be completed faster and more efficiently. The automation is solely to assist the performance of judges in identifying and extracting patterns that lead to the decision of a case. .

  • 2.    Research Method

The data used in the prediction modeling are criminal cases in the Bekasi District Court for 2 years, 2019-2021. The type of data used is secondary data in the form of recapitulation of criminal cases at the Bekasi District Court from January 2019 to January 2021, totaling 1,642 cases in the form of prosecutions for ordinary crimes and decisions based on the length of a sentence with the status of a minutation case. Figure 1 is a sample of raw data before it is processed into machine learning.

The variables used in this research plan are the Letter of Demand (demand) and the Length of the Sentence (Decision). A lawsuit is a letter that makes proof of an indictment based on evidence that has been revealed at trial. In addition, it is the conclusion of the public prosecutor regarding the guilt of the defendant which is accompanied by a criminal charge. The verdict is the detention of a person's freedom for committing a crime. The class is determined based on the maximum length of sentence for each type of crime. If the sentence is 1 year, it will be included in the class for more than one year, if 1 year, it will be included in the class for less than one year.

The software used in this writing is Microsoft Excel 2021, Python, and Anaconda3-2022.05-Windows-x86_64. There are data analysis methods used in this study, namely:

  • 1.    Text Mining, in this case, the data preprocessing used is case folding, remove punctuation, stopword removal, and tokenization. Then for word weighting using TF-IDF.

  • 2.    The SVM method is used to classify and predict the length of sentencing. The SVM used is linear SVM, RBF, sigmoid, and polynomial.

Figure 2 is the research stage in the development of a judge's decision prediction classification model.

ABCD  E   F

G         l       H

KLM NO

773

774

775

771 839PidS'2019PNBks 18-Dec-19 Pengeroyokan yar EKO SUPRAMURBAE ZULFjLDLI Bin JUKRI

Minutasi       54

BIASA    Mengadilt

773 SsS1Pid-BQOlPPNBks 13-Dec-19 Pengeroyokan yar EKO SUPRAMURBAE SUJIYANTO jL1s BALrU Bin JUKI

774 LPidS∞∙2020PNBfcs 02-Jan-20 Tindak Pidana Sen OMAR SYARIF HIDA'YANTO BIN SIDIK

MINUT.LSI          54          BIASA     Mengadili          Supayah-IajelisHakiniPeni              ISOKurangdariltahun

MDJUTASI         43          BIASA     Mengaditi          Supaya Majelis Hakim Peni             240 Kurang dari 1 tahun

776

778

779 4 PidB 2 02OPNBks    13-Jan-2C Penggelapan    FARIZ RACHMAN, SFINDRIETA STIEN MANDAGI all

hΠNUT-ASI        65

BIASA Mengaditi          MenyalakanTerdakwaINE            1275 Lebih dari 1 tahun

776 J1-PidB1QOJCPNBks   15-Jan-20 Penganiayaan   DARSLLH, SH      NDANKjOKOSATRIOaliaiND

777 6 PidSus 2C2CPN Bks 15-Jan-20 Narkotika      EiARSINI. SH       DIMAS AGUNG PANGESTUals I

MINUT.LSI        49

MINtUTASI        35

BLLSA     Mengadili          MenyatakantardakwaNDa             365 Lebih dari 1 tahun

BIASA     hlengaditi          MembehaskanterdakwaDI            2370 Labih dari 1 tahun

779

778 5 PidBQOJOPNBk= 15-Jan-20 Pencurian     DEDETRlANGGRAlIl-DEDYNURDnrANTOBINSLa

MINUT-LSi        28

BIASA    Mengadill

R-Ienyatakan terdakwa DEI             365 Lebihdari 1 tahun

80

81

779 IApidsui1IOlO11PNBks Ib-Jan-JC Narkotika      SlGll MUilAKAM, SkAShPDWilOPO alsAStPbinti

780 11 Pid SbsQOJOPN Bks 16-Jan-2 0 Narkotika      SlGlTMUHARAM-SEM-REZAFEBRIANTOAtsREZ.-

Minutasi       62

MINUT.LSI        55

BIASA    Mengadili

BLLSA    Mengadili

MenyatakanTerdakwaASE            2190 Lebihdari 1 tahun

Menvatakan Terdakwa MI          2555 Lebihdari 1 tahun

82

83

84

781 IC PidBQ 02 OPN Bks 16-Jan-20 Kejahatan Perjudia EKO SUPRAMURBAE EDI SUSILO Bin SUPARIO        MINUTASI        Sl

BIASA    Mengadili

Supaya Majelis Hakim Peni             545 L≤bιh dart 1 tahun

783 J1Pia-D aOaO1PISDKi   IO-Jan-IO PenCKian      JUUT DUVWUUSAn  I-ALiavDuuiSAliDUUNa-JUSa

783 S1PidBQOJO1PNBk-=   16-Jan-2 0 Pemhunuhan    ARIF BUDIMAN.SH  I-DWTPRASETYOAkWTOTTf

OUTlU IJUSl

MINUT.LSI        109

BLLSA    Mengadili

BIASA    Mengadili

Menyatakan terdakwaIjLL             730 Lebihdari 1 tahun

Menvatakan para terdakwa            1825 Lebihdari 1 tahun

s∑

86

87

784 14PidB12020PNBks 20-Jan-20 Pencurian      MELVjUtOSSEN ELLl I-Panji Septiawan Bin Kasim2.W1

hΠNUT-ASI        51

BIASA    Mengadili

Menyatakan Terdakwa I. P             485 Lebih dari 1 tahun

78$ 13 PidBQ 02 OPN Bks 20-Jan-20 Pencurian      SRJ .ASTUTI, SH- RIZKY ALAMSYAH ALS RIZKl

786 3 TPid Sus 2020 PN Bks 21-Jan-20 Narkotika      BAYU All PRAMONC NURWrANTO alias NUE bin MAE

hUNUTASI       44

hUNUTASI       73

.DiJUJi     Oiengaaux          Cupayaoiajeu-SnaKimrenj             VlOLebihdariltahun

BIASA    Meneadili         AgarMajelkHakimPengai           ISSSLebihdariltahun

88

89

90

787 35PidSusQO2O1-PNBks 21-Jan-2 O Narkotika      AKHMADHoTh-IARisENDIFEBRnrADIalsSENDIbit

MINUT-ASI        50

BIASA     Mengadili

A-IenyatakanterdakwaStJI            3650 Lebihdan 1 tahun

788 34, Pid-SuaQOZOiFN Bk* 21-Jan-20 Narkotika      AKHMAD HOTh-LARI MUHAh-IAD FARHAN NOUFaU.

789 BB1PidBQOQOPNBks 21-Jan-2C Kejahatan Perjisdia AKHMAD H0TA∙IAR11.M0H HORI2.ISMAIL,3.SYAEF

hUNUTASI       50

MINUT.LSI        43

BIASA    Mengadili

BLLSA     Meneadili

MenyatakanterdakwaMUJ            3650 Lebihdari 1 tahun

AgarMajelis Hakim Penrat             485 Lebihdari 1 tahun

91

92

93

790 32PidB2020PNBks 21-Jan-20 Kejahatan Perjudra AKHhLAD HOTh-LARI ASMAN,

MINUT-ASI        43

BIASA    Mengadili

Agar Majelis Hakim Pengai             545 Lebih dari 1 tahun

791 31 PtdSus-JOJO-PNBk-S 21-Jan-2 0 Narkotika      AKHMAD HOThLARj BjLSUNI Ati CUNIKETE Bin ID

792 30Pid,SusQ020PNBks 21-Jan-2 0 Narkotika     AKHMADHoTMARISULAIMANBinMISAR

hUNUTASI       T?

MINUT.LSI        57

BIASA    Mengadili

BIASA     Meneadili

MenyatakanterdakwaBAS            2370 Lebihdari 1 tahun

Menvatakan terdakwa SUL            2370 Lebih dari 1 tahun

94

95

96

793 29 PidBQOJO1PN Bk= 21-Jan-2 C Penggelapan    VERoNICASWrUAYLHERLANDHIDAYATBmDARl

hUNUTASI       43

BLLSA     Mengadili

A-Ienvatakan terdakwa HER             730 Lebih dari 1 tahun

794 28Pid.BQ020.PN Bks 21-Jan-20 Pencκian      R-DONNA1SH      MUHAh-IhIADWAHYUDIbinK

795 2TPidSusQ020.PNBks 21-Jan-20 Narkotika      R-DONNA1SH      IUWATAaliasBELONGbinASh

hUNUTASI       50

MINUT.LSI        73

BIASA    Mengadili

BLLSA     Meneadili

Supaya Hakim. Majelis Ha             485 Lebihdari 1 tahun

Agar Majelis Hakim Pengai            2555 Lebih dari 1 tahun

97

98

99

796 2 6PidB2020PNBks 21-Jan-2 0 Pencurian      R-DONNA-SH      MUHAh-IhIADRIZKIBinNAWA

MINUT-ASI        78

BIASA    Mengadili

A-Ienyatakan Perbuatan Ter             455 Lebihdari 1 tahun

797 25iPidB.2020.PN Bki 21-Jan-20 pencurian      MOHAhlAD HARIMZ SATORI Bin IAJULI

798 24, PidBTCIO1PN Bks 21-Jan-20 Penipisan       SATRBrA SUKAiANA DERh-IAWAN AGUNG ALs DEEh

hUNUTASI       50

MINUT.LSI        76

BIASA    Mengadili

BIASA    Mengadili

Meyatakan Terdakwa Satoi             730 Lebih dari 1 tahun

MenvatakanTerdakwaDEl             365 Lebihdari 1 tahun

300

301

302

799 23,Pid-BQOlOTNBks 21-Jan-20 Kejahatan Peijudia ZjhhI ZAMIKHWAN, ULE alias ERWTN bin alm H. MA

MINUT-ASI        55

BIASA    Mengadili

A-Ienyatakan Terdakwa ULl             485 Lebihdari 1 tahun

800 22Pid-Sua-20201'PNBks 21-Jan-20 Lalulintas      SATRΠ'A SUKhIANA HARTONO Bin KAhlSIDI

801 21PidS∞2020PNBks 21-Jan-20 Narfcotika      SATRIYA SUKh-IANA NURDIN Ak INUL Bin NjLSIDIN

hUNUTASI       71

hUNUTASI       43

BLLSA     Mengadili

BIASA    Mengaditi

A-Ienyatakan Terdakwa HA             730 Lebihdan 1 tahun

Menyatakan Terdakwa NU            2920 Lebih dari 1 tahun

RBΛmj≡,Λ'Λ∣'l'Γ∙ °a JL-Jan-JO aarotuα⅛A 1 1A SUAM⅛a⅛ 1Λ1⅛Λ1A⅛ imam Bin imaipi    Minu 1A⅛1 N⅛1ASA Mtaμ⅛⅛-Wenyatafcan Ieroamva

avw Lelnh dan 1 tahun

Figure 1. Sample raw data

Slart

Topic Determination

Making Framework

Literature review

V

Problem

Identification & Formulation

Research purposes

Data Input

Text Preprocessing

_J

V

Word Weighting using TF-IDF

Data Training and Testing

Classification using SVM

End

Figure 2. Research stages

SVM classification used is linear, polynomial, sigmoid, and RBF kernel [9].

Comparisons are needed to determine the accuracy of machine performance in classifying the four kernels. So the best kernel will be found. The stages can be seen in Figure 3.

Preprocessing data


Weighting of

TF-IDF


Data Training and Testing


Calculating SVM kernels


Figure 3. Stages of classification and prediction using SVM


  • 3.    Literature Study

    3.1.    Text mining

Text mining is a process that extracts information in large text collections and automatically identifies interesting patterns and relationships in textual data [10]. Text mining is a combination of several mathematical techniques, statistics, linguistics, data mining, machine learning, and information retrieval. The research stages include preprocessing and text extraction, analysis and statistical processing. Text mining will extract information from a collection of documents. The data used in text mining is a collection of text that has an

unstructured data format, or at least semistructured. The text mining process is divided into 3 main stages, namely, text preprocessing, pattern discovery, and text transformation [11].

  • 1.    Text Processing

This stage is the initial stage in Text Mining. Text preprocessing is done to remove parts or text that are not needed so that they get quality data for execution or so that the mining process is more accurate [12]. In this study, the text preprocessing used is:

  • a.    Case folding: Converts all letters in the document to lowercase.

  • b.    Remove Punctuation: Remove punctuation in document

  • c.    Tokenization: Converts a bunch of text into words

  • 2.    Text Transformation

  • 3.    Text transformation is the process of representing the document. The concept used is the "bag of words" model and the vector space model. This process includes the formation of basic forms of words and reduction of dimensions in the document [13]. In feature generation, there is feature selection, namely the selection of features or the next stage of dimension reduction in the text transformation process. Feature selection used in this study are:

  • a.    Stopwords Removal: Eliminate words that are not characteristic (unique words) from a document.

  • b.    Stemming and Lemmatization: Transforms words contained in a document into root or base words

  • 3.2.    Weighting Term Frequency-Inverse Document Frequency

The Term Frequency-Inverse Document Frequency (TF-IDF) method is a method of assigning weight to the relationship of a word (term) to a document. Term weight is an indicator of the word, because the importance of each word is different [14]. Word weighting is influenced by the following:

  • 1.    Term Frequency (TF) is the weight of the word in the document which is determined from the occurrence of the word. The weight will increase with the number of occurrences of the word.

  • 2.    Inverse Document Frequency (IDF) is a method of checking for word dominance. Common terms can be reduced and words that rarely appear need attention.

  • 3.3.    Support Vector Machine (SVM)

The Support Vector Machine (SVM) by Boser, Guyon, Vapnik was introduced in 1992 [15]. The concept of SVM is how to form the best hyperplane in the input space. The best hyperplane is obtained when the margins and classes are at their maximum point. The outermost data distance from the class and hyperplane is called the margin. The closest pattern in each class is called the support vector [16]. SVM is a variant of the linear machine so it can only be used to solve linearly separable problems. However, in its implementation, linear data problems are rarely obtained. However, with the development of SVM, many SVM cases are non-linear. The solution to this case is to include the concept of a kernel trick in a highdimensional workspace. These computational techniques are kernel tricks formulated in equation (1).

K(XbXj) = Φ(xi).φ(xj)


(1)


The results of the classification of the data obtained equation (2).

/(Φ(x)) = w.Φ(f) + b


(2)


There are various types of functions that can be used as kernel K as shown in Table 1.

Table 1. Various Kernel Functions

Kernel Functions                            Definition

Linear                       K(xbXj) = xi,Xj

Radial Basic Function (RBF)


Sigmoid

Polynomial

  • 4.    Result and Discussion

    4.1.    Descriptive analysis

In this analysis, the researcher describes in general the case statistics report data (http://sipp.pn-bekasikota.go.id/statistik_perkara) of the Bekasi District Court from January 2019 to January 2021 regarding criminal cases. The number of criminal cases in the last 2 years, namely from January 2019 to January 2021, there were 18,326 cases. It is known that from 2019 to 2020 it has decreased. Meanwhile, in 2021 it cannot be explained because the data used is only 1 month data. This means that crimes and violations in Bekasi can be seen to have decreased in the last 2 years.

Criminal cases based on the type of criminal procedure are divided into 3 types of crimes, namely ordinary crimes, short crimes, and also quick crimes. Of the 3 types of criminal proceedings, it can be seen that the type of criminal procedure that has the most cases is ordinary crime. This means that in many cases the final form of the case is a decision. And in this study, researchers only used the type of ordinary criminal procedure, so the data used was 1,860 cases. Of the 1,860 cases based on the type of ordinary criminal procedure, they still had to be classified according to the variable sought with the status of a minutation case, and 123 cases were obtained with other status cases (revocation of cassation case, notification of cassation decision, notification of PK decision, notification of appeal decision, cassation decision, and etc). So that there were 1,737 cases with minutation cases. In addition, this study uses published criminal cases. The published cases are about 95% of the total criminal cases and the unpublished cases are about 5% of the total criminal cases which means the data is disguised. So that it can be seen that the cases that can be published and used by researchers are only 1,642 cases published with minutation cases or about 95% of the total ordinary criminal cases in the Bekasi District Court from January 2019 to January 2021.

The total number of data used for classification is 1642 cases. Where the amount of data for classes more than 1 year is more than the data for classes less than 1 year. This means that in the Bekasi District Court many criminal cases have been detained for more than one year. In Figure 4 is the word cloud of the demand variable. Word Cloud is a word that is often used in all documents, the larger the word size, the more often it is used. It can be seen that the words that are often used in the variable demands are the words 'article', 'paragraph', 'goods', 'evidence', 'amount', 'rp' and so on. There are quite a number of names appearing in the wordcloud because in each document the name is mentioned repeatedly.

Figure 4. Word cloud demand variables


  • 4.2.    Data Preprocessing

The first stage before data classification is preprocessing the data first. It aims to clean and eliminate unnecessary text in the data. There are several stages of data preprocessing used in this study, such as Case Folding, Removing Punctuation, Tokenize, and Stopwords Removal. Figure 5 is the initial data before preprocessing the data.

import pandas as pd

import numpy as np

from wordcloud import WordCloud import matplotlib.pyplot as pit from nltk import FreqDist import seaborn as sns

df = pd.readexcel('DATABARU.xlsx,) df

Out[l]:


STATUS LAMA JENIS ∏1rr∣∣cβu terdakwa perkara proses perkara ’’u™5*"


TUNTUTAN


KLASIFIKASI LAMA         LAMA

ΛLA≡∣r∣r∖M3∣ KURUNGAN   PEMIDANAAN


2


354


NASRULAIs UDABin MARALI


FETRA FARELAIs EMPET

Bin UPIN


salanty koesmanto

alias SALLY


AHMAD HUMAIDI Bin H

SARBINI


i.rian pratamaais

JOKER Bin YOSEP ERWANTO⅛12..


Abdulazizazzabadi

Als REZA Brn ABDUL


Minutasi


Minutasi


Minutasi


Minutasi


Minutasi


Minutasi


61


35


47


BlASA


BlASA


BlASA


BlASA


BIASA


BlASA


Mengsdili


Mengadili


Mengadili


Mengadili


Mengadili


Mengadili


Supaya Majelis Hakim Pengadilan Negeri Bekasi...


Tanpa hak dan melawan hukum membawa, memiliki,...


365


Lebih dari 1 tahun


Menyatakan Terdatava Fetrafarelais EMPETBin..


Percobaan pencurian


1825


Lebih dari 1 tahun


Menyatakan Terdatava SALANTY KOESMANTO aliasa S...


Tindak pidana penipuan


365


Lebih dari 1 tahun


Supaya Majelis Hakim Pengadilan Negeri Kota Be...


Menyatakan Ierdakvra I Rian PratamaAls Joker


Supaya Majelis Hakim

Pengadilan Negeri


Tanpa hak menguasai atau memiliki Narkotika Go...


Pencurian dalam keadaan memberatkan


Tanpa Hak atau melawan hukum


Figure 5. Initial data before preprocessing


1640


300


2005


Lebih dari 1 tahun


Kurang dari 1 tahun


Lebih dari 1


At the case folding stage, all uppercase words are changed to all lowercase letters, especially in the TUNTUTAN and LAMA PEMIDANAAN columns can be seen as in Figure 6. After case folding is done, after that, the characters in words other than letters are deleted, as shown in Figure 7. And Figure 8 is the tokenize result that is applied to the dataset.

In [2]: #case foLding berupa lower case

df['TUNTUTAN’] = df['TUNTUTAN'].apply(lambda x: " ". join(x.lower,() for x in x.split())) df                      ^           ^                     ^                                           ^

Out[2]:

TERDAKWA

STATUS PERKARA

LAMA

PROSES

JENIS PERKARA

PUTUSAN

TUNTUTAN

KLASIFIKASI

LAMA KURUNGAN

LAMA PEMIDANAAN

0

NASRUL Als UDA Bin

MARALI

Minutasi

63

BIASA

Mengadili

supaya majelis hakim pengadilan negeri bekasi

Tanpa hak dan melawan hukum membawa, memiliki,...

365

Lebih dari 1 tahun

1

FETRA FAREL Als EMPET Bin UPIN

minutasi

35

BIASA

Mengadili

menyatakan terdakwa fetra farel als empet bin...

Percobaan pencurian

1825

Lebih dari 1 tahun

2

SALANTY KOESMANTO alias SALLY

minutasi

63

BIASA

Mengadili

menyatakan terdata salanty koesmanto aliasa s...

Tindak pidana penipuan

365

Lebih dari 1 tahun

3

AHMAD HUMAIDI Bin H

SARBINI

minutasi

61

BIASA

Mengadili

supaya majelis hakim pengadilan negeri kota be...

Tanpa hak menguasai atau memiliki Narkotika Go...

1640

Lebih dari 1 tahun

4

1 .rian pratamaais JOKER Bin YOSEP

ERWANT0∖n2....

minutasi

35

BIASA

Mengadili

menyatakan terdakwa i rian pratama als joker b...

Pencurian dalam keadaan memberatkan

300

Kurang dari 1 tahun

354

ABDULAZIZ AZZABADI Als

REZABinABDULBASIT

MINUTASI

47

BIASA

Mengadili

supaya majelis hakim pengadilan negeri bekasi...

Tanpa Hak atau melawan hukum menjadi perantara..

2005

Lebih dari 1 tahun

355

firmansyah ais empe BinAlmACASUDlRJA

MINUTASI

76

BIASA

Mengadili

supaya majelis hakim pengadilan negeri bekasi...

Tanpa hak atsu melawan hukum menjual, membeli

2555

Lebih dari 1 tahun

ARDi Wibowoals

supaya majelis hakim

Tindakpidana dalam

Lebih dari 1

Figure 6. Case folding results

Ir [3]:


Out[3]:


%menghapus karakter selain huruf

df['TUNTUTAN'] = df['TUNTUTAN'].str.replace('[λ∖w∖s]', ") df['TUNTUTAN'] = df['TUNTUTAN'].str.replace('∖d+'j ") import string

printable = set(string.printable)

def remove_spec_chars(in_str):

return ''.join([c for c in instr if c in printable])

df['TUNTUTAN'].apply(remove_spec_chars)

df ['TUNTUTAN'].head(10)

C:\Users\Jeje\AppData\Local\Temp\ipykepnel_7340\2134506378.py:2: FutureWarning: The default value of regex will change froιr e to False in a future version.

df[,TUNTUTAN’] = df [TUNTUTAN'], str. replace('[A\w\s]',")

C:\Users\Jeje\AppData\Local\Temp\ipykernel_7340\2134506378.py:3: FutureWarning: The default value of regex will change froιτ e to False in a future version.

df['TUNTUTAN'] = df[ TUNTUTAN'].str.replace('∖d+'j ")

0    supaya majelis hakim pengadilan negeri bekasi ..

  • 1    menyatakan terdakwa fetra farel als empet bin ..

  • 2    menyatakan terdakwa salanty koesmanto aliasa s..

  • 3    supaya majelis hakim pengadilan negeri kota be..

  • 4    menyatakan terdakwa i rian pratama als joker b..

  • 5    menyatakan terdakwa samuhaji bin jasmin terbuk..

  • 6    menyatakan terdakwa fatur rahman bin syahrudin..

  • 7    menyatakan terdakwa anggi banur destian bin ba..

S    menyatakan terdakwa dedi wahyudi bin supendi t..

  • 9    menyatakan terdakwa aan febriansyah bin saan t..

Name: TUNTUTANj dtype: object

Figure 7. Results of removing punctuation

In [4]: # Menerapkan fungsi Tokenize pada Dataset

import nltk

πltk.download('punkt1)

def identify_tokens(row): text = row

tokens = nltk.word_tokenize(text)

# taken only words (not punctuation)

tokenwords = [w for w in tokens if w.isalpha()] return token_words

df[,token'] = df['TUNTUTAN'].apply(identify_tokens)

df[,token’].head(5)

[nltk-data] Downloading package punkt to

[nltk-data] C:\Users\Jeje\AppData\Roaming\nltk_data...

[nltk-data] Package punkt is already up-to-date!

0ut[4]: e    [supaya, majelis, hakim, pengadilan, negeri, b...

  • 1    [menyatakan, terdakwa, fetra, farel, als, empe...

  • 2    [menyatakan, terdakwa, salanty, koesmanto, ali...

  • 3    [supaya, majelis, hakim, pengadilan, negeri, k...

  • 4    [menyatakan, terdakwa, i, rian, pratama, als, ...

Name: token, dtype: object

Figure 8. Tokenize results

Furthermore, in the text transformation at the stopwords removal stage, words that are not important or meaningless and not included in the dictionary will be deleted, such as 'dia', 'dua', 'ia', 'seperti', 'jika', and so on, can be seen in Figure 9.

stop-+actory = StopwordRemoverFactoryO

data-stopword = stop_factory.get_stop_words()

stopword = stop_factory.create_stop_word_remover()

print(datastopword)

Requirement already satisfied: sastrawi in c:\users\jeje\anaconda3\lib\site-packages (l.θ.l)

[,yang', 'untuk', ,pada', 'ke,, ,para’, 'namun', ‘menurut’, 'antara', ,dia', ,dua,, 'ia', 'seperti', 'jika', 'jika', 'sehingg a’, 'kembali', 'dan', 'tidak', 'ini', 'karena', 'kepada', 'oleh', 'saat', 'harus', ’sementara’, 'setelah', ’belum’, 'kami', 'se kitar', 'bagi', 'serta', 'di', 'dari', 'telah', 'sebagai', 'masih', 'hal', 'ketika', 'adalah', 'itu', 'dalam', 'bisa', 'bahwa', 'atau', 'hanya', 'kita', 'dengan', 'akan', 'juga', 'ada', 'mereka', 'sudah', ’saya', 'terhadap', 'secara', 'agar', 'lain', 'and a’, 'begitu', ’mengapa', 'kenapa', 'yaitu', ’yakni', 'daripada', 'itulah', 'lagi', 'maka', 'tentang', 'demi', 'dimana', 'keman a', 'pula', 'sambil', 'sebelum', 'sesudah', 'supaya', 'guna', 'kah', 'pun', 'sampai', 'sedangkan', 'selagi', 'sementara', 'teta pi', 'apakah', 'kecuali', 'sebab', 'selain', 'seolah', 'seraya', 'seterusnya', 'tanpa', 'agak', 'boleh', 'dapat', 'dsb', 'dst', 'dll', 'dahulu', 'dulunya', 'anu', 'demikian', 'tapi', 'ingin', 'juga', 'nggak', 'mari', 'nanti', melainkan', 'oh', 'ok', 'seh arusnya', 'sebetulnya', 'setiap', 'setidaknya', 'sesuatu', 'pasti', 'saja', 'toh', ’ya', 'walau', 'tolong', 'tentu', 'amat', ‘a palagi', 'bagaimanapun']

In [6]: # Menerapkan fungsi Stopword pada Dataset1 dan membuat kotom Stopmrds def stop_list(row): my_list = row stoplist = [stopword.remove(i) for i in mylist] return (stoplist)

In [7]: df [ 'stop_words'] = df['token'].apply(stop-list) df['stopwords'].head(5)

0ut[7]: θ [, majelis, hakim, pengadilan, negeri, bekasi,...

  • 1    [menyatakan, terdakwa, fetra, farel, als, empe...

  • 2    [menyatakan, terdakwa, salanty, koesmanto, ali...

  • 3    [, majelis, hakim, pengadilan, negeri, kota, b...

  • 4    [menyatakan, terdakwa, i, rian, pratama, als, ...

Name: stopwords, dtype: object

Figure 9. Stopwords removal results

  • 4.3.    Weighting of TF-IDF

After preprocessing the data, in Figure 10 is a sample of the words used to show the results of the TF-IDF word weighting.

Figure 10. Weighting of TF-IDF results

  • 4.4.    SVM Modeling

Before classifying using SVM, we first determine the training data and testing data. In this study, the distribution of training data and testing data is 80:20. With 1313 training data and 329 testing data. The distribution of data can be seen in Table 2.

Table 2. Splitting of training and testing data

Class

Data Training

Data Testing

Total

More than 1 year

1198

300

1498

Less than 1 year

115

29

144

Total

1313

329

1642

After splitting training and testing data, modelling the classification using SVM. In this study, used 4 kernels, namely linear, RBF, Sigmoid, and Polynomial as well as several choices of parameters C = 0.1, 1, 10, 100, 1000 and gamma parameters = 1, 0.1, 0.01, 0.001, 0.0001. To get the best parameters from several parameter choices, uses a tuning process which then looks for the best accuracy value from the 4 kernels. The modeling process can be seen in Figure 11 and the results of the comparison of the four SVM kernels can be seen in Table 3.

Based on the accuracy value generated by the SVM model using a different kernel, a fairly good accuracy is obtained with the smallest accuracy of 88.4% using the RBF and Sigmoid kernels. The use of Linear and Polynomial kernels increases their accuracy, the use of Linear kernels has the highest accuracy of 89.4% and the Polynomial kernel has a lower accuracy of 89.1%.

In [18]: Kmelakukan prediksi terhadap data K-Test

y_pred - clf.predlct(X.tMt)

t*elakukan evaluasi Knggunakan cunfosion matrix

free sklwrn.∙∙trici Iaport cla$sification_report, Confuslonjutrlx PrintCConfusion Matrix svm")

print (conf us Ionjut r ix (y_test, y_pred))

prInt(classIfication-report(y_test1y_pred))

free Sklearn.metrics inport accuracyscore

acu_dt-accuracy_score(y_test, y_pred)

κhasil akurasi menggunakan SVM

print (‘AKURASI SVM: X.3f X acu-dt)∣


In [20]: Oieelaitukan prediksi ttrhodap data X-Test y_pred ∙ clf.predict(X.test)

KmeLakukan evaluasi menggunakan cunfosion matrix from Sklearn.metrics inport classlfication_report, Confusionjutrix print("Confusion Matrix SVM Kernel rbf") print (confusion jratrix(y_test,y_pred)) print(classification_report(y_test,y_pred)) from sklearn.metrics import accuracy-score acu_dt-accuracy_score(y_test, y_pred)

Khasil akurasi menggunakan SVM

PrintCAKURASI SVM: X.3f X acu.dt)


Confusion Matrix SVM (I 3 35]

I e »1)1

precision

recall fl-score

support

Kurang dari 1 tahun

1.88

0.08     0.15

38

Lebih dari 1 tahun

0.89

1.00     0.94

291

accuracy

0.89

329

macro avg

0.95

Θ.S4     0.54

329

weighted avg

AKURASI SVM: 8.894

8.91     8.89     0.85

(a) Kernel Linear

329


Oιt(21): SVC(kernel-‘sigmoid')


Confusion Matrix SVM [( e 38] ( ∙ 291]]

Kernel rbf precision

recall fl

■score

support

Kurang dari 1 tahun Leblh dari 1 tahun

8.00

0.88

0.00

1.00

0.00

0.94

38

291

accuracy macro avg weighted avg

0.44

0.78

0.50

0.88

0.88

0.47

0.83

329

329

329

AKURASI SVM: 0.884

CutpS] SVC (kernel ■’ poly')

(b) Kernel RBF


In [22]: KeLokuhan prediksi terhadap data X-Test yj>red ∙ clf.predict(X_test)

Kelokuhan evaluasi Oenggunakan cunfosion matrix from skleam.∙etrics inport classification report, Confusionjutrlx print(’Confusion Matrix SVH Kernel sigmoid*) print(confusionjMtrlx(y_test,y_pred)> print(cIassifIcation_report(y_test,y_pred)) from Skleam1Betrics import accuracy_score acu-dt -accuracy_score(y_test, y j>red)

Ohasil akurasi menggunakan SVM

PrintCAKURASI SVM: X.3f X acu.dt)


Confusion Matrix SVM Kernel signoid

[[ β ≡8]

( 0 291]] precision recall fl-score support


Kurang Oari 1 tahun       0.00     0.00     0.0038

Lebih Carl I tahun      0.88      1.00      0.94291

accuracy                           0.88329

■aero avg      0.44      0.58     0.47329

weighted avg      0.78     0.88      0.83329


AKURASI SVM: 0.884


(c) Kernel Sigmoid


In [26]: KeLakukan prediksi terhadap dcta X-Test yj>red - clf.predict(X_te»t)

Kelekuhan eva'.uasi Benggunekar cunfosion matrix

from sklearn.metric< iιιpnrt rIuccifir»t1en_rApnrr. r<>afIicinnjutrix print("Confusion Matrix SVM Kernel Polynomlil")

P lni(iiofu»luiijMirlx(y_ie>l,)_pr«tl)) ρ-int(cJassifIcation j,eport(y-test,yj>red)) from Sklearn1Inetrics import accuracy-score a:u_dt-«ccurac/_s<ore(y_test, >_pred)

erasit akurasi menggunakan svm

p'ln*.(,iKURASI SVN: X.3f X aCL_dt)

Confusion Matrix SVM Kernel Polyncmial [[ 3 35)

precision recall fl-score   support

Kurang dari 1 tahun       A 75      A RR      A 1438

.ebih dari 1 tahun       θ.89      1.00      8.94291

accuracy                          8.89329

macro avg      8.82     8.54     8.54329

Wmifttmd avg      0.88     0.89     0.85320

AcuκASi SVH: 8.89; (d) Kernel Polynomial


Table 3. Kernels comparison results on SVM

Kernel

Accuracy

Linear

89,4 %

RBF

88,4%

Sigmoid

88,4%

Poynomial

89,1%

Figure 11. Process and results of SVM modeling with 4 kernels

  • 5. Conclusion

In the research to predict the judge's decision at the Bekasi District Court, the data used is the number of criminal cases from January 2019 to January 2021. From 18,326 criminal cases based on the type of criminal procedure, they are divided into 3 types of crimes, namely ordinary crimes, short crimes, and also quick crimes. In this study using the type of ordinary criminal procedure as many as 1,860 cases, but after being classified according to the variables sought with the status of minutation cases, the number of cases became 1,737 and only 1,642 criminal cases were published. Processing data using python with preprocessing data used are case folding, remove punctuation, stopword removal, and tokenization. Then for word weighting using TF-IDF. The SVM method is used for classification and prediction of the length of punishment, before modeling the data is split with a ratio of 80:20 and the results of the comparison of classification modeling using SVM with 4 kernels are linear (89.4%), rbf (88.4%), sigmoid (88 ,4%), and polynomials (89,1%). The results obtained that have the best kernel is a

linear kernel with an accuracy value of 89.4% and an error value of 10.6%. With the results of classification modeling using SVM which is quite high, it can be used as a judge's tool in predicting the length of sentencing at trial.

Acknowledgments

The authors would like to thank the Ministry of Education, Culture, Research and Technology (Kemdikbudristek) of the Republic of Indonesia for funding research in 2022.

References

  • [1]    M. Wijaya, “Revolusi Industri 4.0: Implikasi terhadap Manajemen Sumberdaya

Manusia,” Media Inform., vol. 19, no. 2, pp. 51–60, Aug. 2020, doi: 10.37595/mediainfo.v19i2.41.

  • [2]    M. N. Angelo Basile, Gareth Dwyer, Maria Medvedeva, Josine Rawee, Hessel Haagsma,

“N-gram: New groningen author-profiling model,” Noteb. PAN CLEF, 2017.

  • [3]    L. Mai and E. Boulot, “Harnessing the transformative potential of Earth System Law:

From theory to practice,” Earth Syst. Gov., vol. 7, p. 100103, Mar. 2021, doi: 10.1016/j.esg.2021.100103.

  • [4]    S. D. Kebede and Z. Tiewei, “Public work contract laws on project delivery systems and

their nexus with project efficiency: evidence from Ethiopia,” Heliyon, vol. 7, no. 3, p. e06462, Mar. 2021, doi: 10.1016/j.heliyon.2021.e06462.

  • [5]    M. Derlln and J. Lindholm, “Serving Two Masters: CJEU Case Law in Swedish First

Instance Courts and National Courts of Precedence As Gatekeepers,” SSRN Electron. J., 2017, doi: 10.2139/ssrn.2952783.

  • [6]    K. A. Olsen HP, “Finding hidden patterns in ECtHR’s case law: On how citation network

analysis can improve our knowledge of ECtHR’s Article 14 practice,” Int J Discrim Law, vol. 17, no. 1, pp. 4–22, 2017.

  • [7]    E. Mumcuoğlu, C. E. Öztürk, H. M. Ozaktas, and A. Koç, “Natural language processing

in law: Prediction of outcomes in the higher courts of Turkey,” Inf. Process. Manag., vol. 58, no. 5, p. 102684, Sep. 2021, doi: 10.1016/j.ipm.2021.102684.

  • [8]    J. E. Davidson et al., “Job-Related Problems Prior to Nurse Suicide, 2003-2017: A Mixed

Methods Analysis Using Natural Language Processing and Thematic Analysis,” J. Nurs. Regul., vol. 12, no. 1, pp. 28–39, Apr. 2021, doi: 10.1016/S2155-8256(21)00017-X.

  • [9]    A. P. Nugraha, I. N. Piarsa, and I. M. Suwija Putra, “Comparison of Support Vector

Machine and K-Nearest Neighbor for Baby Foot Identification based on Image Geometric Characteristics,” J. Ilm. Merpati (Menara Penelit. Akad. Teknol. Informasi), p. 84, Apr. 2021, doi: 10.24843/JIM.2021.v09.i01.p08.

  • [10]    S. Kumar, A. K. Kar, and P. V. Ilavarasan, “Applications of text mining in services management: A systematic literature review,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 1, p. 100008, Apr. 2021, doi: 10.1016/j.jjimei.2021.100008.

  • [11]    M. Lee, S. Kim, H. Kim, and J. Lee, “Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph,” Technol. Forecast. Soc. Change, vol. 180, p. 121718, Jul. 2022, doi: 10.1016/j.techfore.2022.121718.

  • [12]    M. O. Hegazi, Y. Al-Dossari, A. Al-Yahy, A. Al-Sumari, and A. Hilal, “Preprocessing Arabic text on social media,” Heliyon, vol. 7, no. 2, p. e06191, Feb. 2021, doi: 10.1016/j.heliyon.2021.e06191.

  • [13]    E. S. Tellez, S. Miranda-Jiménez, M. Graff, D. Moctezuma, O. S. Siordia, and E. A. Villaseñor, “A case study of Spanish text transformations for twitter sentiment analysis,” Expert Syst. Appl., vol. 81, pp. 457–471, Sep. 2017, doi: 10.1016/j.eswa.2017.03.071.

  • [14]    A. Mee, E. Homapour, F. Chiclana, and O. Engel, “Sentiment analysis using TF–IDF weighting of UK MPs’ tweets on Brexit,” Knowledge-Based Syst., vol. 228, p. 107238, Sep. 2021, doi: 10.1016/j.knosys.2021.107238.

  • [15]    N. P. R. Apriyanti, I. K. G. D. Putra, and I. M. S. Putra, “Peramalan Jumlah Kecelakaan Lalu Lintas Menggunakan Metode Support Vector Regression,” J. Ilm. Merpati (Menara Penelit. Akad. Teknol. Informasi), p. 72, Jun. 2020, doi: 10.24843/JIM.2020.v08.i02.p01.

  • [16]    I. P. A. P. Wibawa, I. K. A. Purnawan, D. P. S. Putri, and N. K. D. Rusjayanthi, “Prediksi Partisipasi Pemilih dalam Pemilu Presiden 2014 dengan Metode Support Vector Machine,” J. Ilm. Merpati (Menara Penelit. Akad. Teknol. Informasi), p. 182, Dec. 2019, doi: 10.24843/JIM.2019.v07.i03.p02.

Comparison of Kernel Support Vector Machine in Predicting Judges’ Decisions at the Bekasi 154

District Court (Harry Dwiyana Kartika)