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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Informatika Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Journal of Economic, Bussines and Accounting (COSTING) Jurnal Sains dan Informatika Jurnal Informatika Universitas Pamulang Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer bit-Tech Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Bulletin of Informatics and Data Science
Gusti, Siska Kurnia
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Klasifikasi Sentimen Transformasi dan Reformasi Sepak Bola Indonesia Pada Twitter Menggunakan Algoritma Bernoulli Naïve Bayes Destri Putri Yani; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5829

Abstract

Federation Internationale de Football Association (FIFA) carried out Transformations and Reformations to Indonesian Football with one of them Indonesia was chosen as the Host of the U-20 World Cup in 2023. The transformations and reformations carried out cause people to often provide opinions through social media Twitter. Opinions given by the public can be positive or negative. The research uses Text Mining to classify sentiment in 2 categories with the Bernoulli Naïve Bayes algorithm. This research aims to classify positive and negative sentiments and determine the level of accuracy value of the sentiment classification results of Indonesian Football Transformation and Reformation. The research stages carried out are data collection, text preprocessing, data labeling, TF-IDF weighting, Bernoulli Naïve Bayes classification, and evaluation. Based on the research results from 4907 data there is duplicate data and only uses 2125 data which is divided into 90% training data and 10% testing data, so as to get accuracy with a high category value of 88%. The classification results show that many tweets are positive sentiments.
Klasifikasi Sentimen Tragedi Kanjuruhan Pada Twitter Menggunakan Algoritma Naïve Bayes Iqbal Salim Thalib; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5852

Abstract

The Kanjuruhan Malang incident occurred on October 1 and resulted in 132 deaths, 96 serious injuries and 484 minor injuries. The cause of the riot occurred due to provocation between Arema Malang supporters and Persebaya Surabaya supporters who mentioned harsh words and other provocative actions that caused anger on both sides. Sentiment analysis of the Kanjuruhan tragedy using the Naive Bayes method was conducted through tweets taken through Twitter to understand the public's perception of the incident. The Naïve Bayes algorithm is performed for the sentiment classification of tweet data which is applied by processing the tweet text and classifying it into positive, negative, and neutral. In this study using data as much as 4843 data and carried out with tweet data that has been crawled resulting in 2,042 data. This research aims to classify sentiment and determine the level of accuracy in the Multinomial Naïve Bayes algorithm in the Kanjuruhan tragedy using a dataset in the form of tweets from twitter social media. The processed tweet data is divided into two types, namely 90% training data and 10% test data.  The results of this classification get a Naïve Bayes accuracy of 75% with a precission of 73%, recall of 75%, and f1-score value of 74%. The results of the tweet data used in this study can be concluded that the Naïve Bayes algorithm has a fairly good accuracy value.
Penerapan Algoritma C4.5 Mengklarifikasi Penerimaan Bantuan Sosial Menggunakan Feature Selection M Wandi Dwi Wirawan; Siska Kurnia Gusti; Jasril Jasril; Pizaini Pizaini
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 1 (2023): September 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6653

Abstract

The Indonesian government's efforts to overcome poverty in Indonesia are through the Smart Indonesia Card (KIP) program which is carried out by the government in the form of providing assistance to underprivileged families. The main aim of distributing KIP assistance is to help send underprivileged children to continue their education, the difficulties found in receiving KIP are due to the large number of residents registering, as well as the data having several conditions, the limited time available in providing KIP by sub-district parties, the completion base is relatively low, therefore the provision of assistance must be right on target. Therefore, the aim of this research is to look for the most influential attributes in receiving KIP assistance in order to improve the results of the data verification process. After carrying out Feature Selection using Information Gain, the most influential attributes can be obtained. The influences are Number of Art, Number of Rooms, Cooking Room, Refrigerator, Motorbike. Therefore, we need to know some of the attributes that most influence the selection of KIP assistance so that we can get accuracy values from decision tree modeling using the C4.5 algorithm or decision tree. Test This experiment can produce a decision tree in which the Number of Art attribute is the most influential attribute with the success rate of KIP acceptance. This evaluation uses a confusion matrix to obtain an accuracy value of 98.21%, precision of 98.21%, recall of 99.48%.
Penerapan Seleksi Fitur Untuk Klasifikasi Penerima Bantuan Sosial Pangkalan Sesai Menggunakan Metode K-Nearest Neighbor Muhammad Fauzan; Siska Kurnia Gusti; Jasril Jasril; Pizaini Pizaini
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 1 (2023): September 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i1.6654

Abstract

The inability to fulfill basic human needs is how poverty is defined. To address this issue, the indonesian goverment implements various social assistance programs, one of which is Kartu Indonesia Pintar (KIP), aimed at providing free education to children aged 7-18 who are economically disadvantaged. However, in the distribution of aid in the Pangkalan sesai sub-district, distributing officers often face challenges due to the high number of eligible recipients applying, complex data requierements, and limited time for the officers. Distributing this social assistance accurately is crusial. Therefore, this research aims to determine the accuracy value for the data of potential recipients of the Kartu Indonesia Pintar (KIP to enhance the data verification process’s outcomes. To tackle this issue, the research employs the K-Nearest Neighbor (K-NN) algoritm and also employs feature selection using Information Gain to reduce less influential attributes. The data used consists of 1998 records of KIP beneficiaries from the 2023 in excel format, with 33 attributes. After performing data cleaning an Information Gain-based feature selection, the dataset is reduced to 1675 records, with 5 selected attributes. The best classification result in this study is achieved with ratios of 7:3 and 8:2, and a value of k = 5, yielding the highest accuracy of 98,21%. The lowest accuracy is obtained using a ratio of 9:1 with the same k value when not using Information Gain, resulting in an accuracy of 89,82%.
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma Naïve Bayes Syahbudin Hamwar; Alwis Nazir; Siska Kurnia Gusti; Iwan Iskandar; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 2 (2023): Desember 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i2.7038

Abstract

Livestock is becoming one of the important animal protein source providers, along with the fisheries sector, to meet the protein needs of the community at large. One type of livestock business that is popular is the maintenance of broiler chickens because of the potential for meat yield. Today, many breeders run a partnership pattern with large companies where breeders play the role of the main supplier and the company as the core. This step helps maintain the stability of production and income of farmers. The success of farmers in broiler chicken production can be measured by looking at the performance index (IP), if the performance is not good then coaching from the core company is needed. The large amount of data obtained from farmers makes it difficult for core companies to model the success rate of farmer production, this can make it difficult for core companies to choose farmers who need coaching. The application of data mining methods using the Naïve Bayes algorithm classification model has the potential to provide solutions to this problem. The purpose of this study was to predict how much success rate of broiler chicken production in Riau region by utilizing the Naïve Bayes Classifier algorithm. This study utilizes a production data set involving 952 broiler chicken farmers in Riau, with 3 scenarios dividing the data ratio of 90:10, 80:20, and 70:30. The results of the analysis showed that through the evaluation of the confusion matrix, it was best found in a data ratio of 90:10 with accuracy results reaching 89,58%, precision reaching 89,89%, and recall reaching 90,16%.
Analisis Komparatif Jarak Euclidean, Manhattan, Canberra, Chebyshev, Cosine pada K-Means untuk Evaluasi Kepuasan Masyarakat Fakhri Fakhri; Iis afrianty; Elvia Budianita; Fadhilah Syafria; Siska Kurnia Gusti; Salmiyati Salmiyati
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10086

Abstract

The selection of distance metrics in the K-Means Clustering algorithm can affect the quality of clustering results, particularly on public satisfaction data measured using a Likert scale. This study aims to compare the performance of five distance metrics, namely Euclidean Distance, Manhattan Distance, Canberra Distance, Chebyshev Distance, and Cosine Similarity, in clustering the level of public satisfaction toward public services. The research data were obtained from 533 respondents who used the services of the Mal Pelayanan Publik (MPP) Pekanbaru through a questionnaire consisting of 23 questions based on the SERVQUAL dimensions and the Community Satisfaction Survey indicators in accordance with PERMEN PAN-RB Number 14 of 2017. After the data cleaning process, one duplicate record was removed, resulting in 532 respondent records used in the analysis stage. The number of clusters was determined using the Elbow Method, while cluster quality was evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. The results show that Manhattan Distance with k=2 produced the lowest DBI value of 0.8144, whereas Euclidean Distance with k=3 produced the highest Silhouette Score of 0.5088. The clustering results formed groups of respondents with different satisfaction levels, namely Dissatisfied, Satisfied, and Very Satisfied. This study contributes an evaluative comparison of five distance metrics in the K-Means algorithm using two evaluation approaches simultaneously, namely the Davies-Bouldin Index and Silhouette Score, on public satisfaction data based on a Likert scale. The results indicate that the performance of distance metrics may differ depending on the evaluation method used, therefore the selection of distance metrics should consider the characteristics of the data and the objectives of the analysis.The difference in evaluation results indicates that DBI and Silhouette Score assess clustering quality from different aspects. Based on the findings, Manhattan Distance and Euclidean Distance demonstrated better performance compared to other distance metrics on the dataset used, and can therefore be considered in the analysis of public satisfaction toward public services.
Penerapan Metode ADASYN Dalam Mengatasi Imbalanced Data Untuk Klasifikasi Penyakit Stroke Menggunakan Support Vector Machine Alwaliyanto Alwaliyanto; Siska Kurnia Gusti; Iis Afrianty; Fadhilah Syafria
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.612

Abstract

Stroke is one of the leading causes of death and disability worldwide, making it essential to develop classification models that can assist in early and accurate diagnosis. This study aims to implement the Support Vector Machine (SVM) algorithm with three types of kernels linear, polynomial, and Radial Basis Function (RBF) to classify stroke disease data. The Adaptive Synthetic Sampling (ADASYN) method is employed to address the class imbalance problem, while model training and evaluation are carried out using 5-Fold Cross-Validation to ensure stable and reliable results. The findings indicate that ADASYN successfully improves the model’s sensitivity to stroke cases (the minority class), as reflected by an increase in recall and F1-score, despite a slight decrease in overall accuracy a common trade-off in handling imbalanced data. The linear kernel (after ADASYN) achieved the best performance after imbalance handling, with an average AUC-ROC of 0.8333, recall of 0.7827, and F1-score of 0.2181 for the stroke class. Although the F1-score remains relatively low, it improved compared to the pre-ADASYN results, indicating better detection of stroke cases. The implementation was conducted using Google Colab, which also contributed to efficient data processing and visualization. Overall, the results demonstrate that the combination of SVM and ADASYN is effective in enhancing the model’s sensitivity to minority classes and is well-suited for medical data classification tasks, particularly in the early diagnosis of stroke using machine learning approaches.
Penerapan Information Gain Untuk Seleksi Fitur Pada Klasifikasi Jenis Kelamin Tulang Tengkorak Menggunakan Backpropagation Nada Tsawaabul Khair; Iis Afrianty; Fadhilah Syafria; Elvia Budianita; Siska Kurnia Gusti
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.637

Abstract

Forensic anthropology and skull analysis play a crucial role in the biological identification of individuals, including sex determination. This study aims to improve the accuracy of gender classification based on skull structure by combining the Information Gain feature selection method with the Backpropagation algorithm. The dataset used is the craniometric data compiled by William W. Howells, consisting of 2,524 samples with 85 measurement features. The preprocessing stage includes data selection, data cleaning, and normalization. Feature selection was conducted using the Information Gain method with three threshold values: 0.01, 0.05, and 0.1, resulting in 79, 46, and 38 selected features, respectively. The model was evaluated using the K-Fold Cross Validation method with K=10 and K=20. The highest accuracy of 93.91% was achieved at the 0.01 threshold using the Backpropagation architecture [79:119:1], a learning rate of 0.01, and K=20. These results demonstrate that feature selection using Information Gain enhances the performance of the Backpropagation model by eliminating irrelevant features and minimizing the risk of overfitting.
Perbandingan Teknik Penyeimbang Kelas Pada Multi-Layer Perceptron (MLP) Berbasis Backpropagation Untuk Klasifikasi Diabetes Mellitus Robby Azhar; Siska Kurnia Gusti; Iis Afrianty; Elvia Budianita
Bulletin of Computer Science Research Vol. 5 No. 6 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i6.804

Abstract

Diabetes Mellitus (DM) is a chronic disease that can lead to serious complications if not detected early; therefore, early diagnosis is highly important. One of the methods that can be applied for early diagnosis is the classification technique in data mining. However, the classification process often faces challenges due to class imbalance, which can reduce model performance. This study aims to analyze the effect of class balancing techniques on the performance of the Backpropagation Neural Network (BPNN) in classifying DM cases. BPNN is a form of Multi-Layer Perceptron (MLP) with a simple structure and the ability to solve complex problems with good accuracy. The dataset used in this study is the Pima Indians Diabetes Dataset, consisting of 768 instances, including 500 non-diabetic and 268 diabetic cases. The research was conducted using three scenarios: without balancing, Synthetic Minority Over-sampling Technique (SMOTE), and Random Under Sampling (RUS). The BPNN model was designed with two architectural variations (one hidden layer and two hidden layers), three learning rate values (0.1, 0.01, and 0.001), and a varying number of neurons. The dataset was divided using the 10-Fold Cross Validation technique. The results show that applying SMOTE achieved the best performance, with an average accuracy of 90.89%, precision of 91.22%, recall of 90.89%, and F1-score of 90.89% on the BPNN architecture with one hidden layer. Furthermore, the single hidden layer architecture proved more stable than the two hidden layers, especially when the dataset size decreased due to RUS. Therefore, the combination of SMOTE and BPNN with one hidden layer provides better performance in classifying Diabetes Mellitus cases.
Penerapan Seleksi Fitur Information Gain dan Metode Backpropagation Neural Network Untuk Klasifikasi Atrisi Karyawan Dinyah Fithara; Elvia Budianita; Iis Afrianty; Siska Kurnia Gusti
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.922

Abstract

Employee attrition management is a critical challenge for organizations as it involves costs, time, and the risk of decision-making errors. This problem requires a data-driven business strategy to achieve more accurate predictions of employees who are potentially at risk of termination. This study applies the Information Gain feature selection method and the Backpropagation Neural Network (BPNN) algorithm in the employee attrition classification process with the aim of increasing the accuracy and efficiency of the prediction model. BPNN is chosen due to its simpler architecture, faster training time, and greater stability for small to medium sized datasets.  With the assistance of Information Gain feature selection, BPNN is able to achieve optimal performance without requiring a complex architecture. The dataset used consist of 35 attributes and 1.470 employee records covering various factor such as age, income level, and employment status. The research stages include feature selection based on information gain values with specific thresholds, data partitioning using k-fold cross validation, and model training using BPNN with variations of learning rates and hidden neuron counts. The results show that the combination of Information Gain and BPNN improves classification accuracy compared to models without feature selection, achieving the highest average accuracy of 87.28% when using 25 selected attributes, with a BPNN configuration of learning rate 0.001, 35 hidden neurons, and 50 epochs. The attributes with the highest Information Gain score include JobLevel, OverTime, MaritalStatus, and MonthlyIncome. This study demonstrates that the proposed approach successfully enhances the prediction performance of employee attrition and can serve as a foundation for developing data-driven models that support employee retention efforts.
Co-Authors Abdul Wahid Abdullah Abdullah Abdullah, Said Noor Abdussalam Al Masykur Adi Mustofa Al Rasyid, Nabila Alfaiza, Raihan Zia Alfin Hernandes Alwaliyanto Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Amelia, Felina Anggi Vasella Azhima, Mohd Baehaqi Beni Basuki Citra Wulandari Cut Lira Kabaatun Nisa Destri Putri Yani Devi Julisca Sari Dina Septiawati Dinyah Fithara efni humairah Eka Pandu Cynthia Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Erni Rouza, Erni Fadhilah Syafria Fakhri Fakhri Faridatul Jannah Faska, Ridho Mahardika Febi Yanto Fitri Insani Fitri Insani Fitri Wulandari Fitri, Anisa Gusti, Gogor Putra Hafi Puja Iis Afrianty Iis Afrianty Iqbal Salim Thalib Irsyad (Scopus ID: 57204261647), Muhammad Iwan Iskandar Jasril Jasril Jasril Jasril Juliandi Kurniansyah Lestari Handayani lis Afrianty M Wandi Dwi Wirawan Maemonah, Maemonah Morina Lisa Pura Muhammad Affandes Muhammad Affandes Muhammad Fauzan Muhammad Fikry Muhammad Hafiz Muhammad Irsyad Muhammad Khairy Dzaky Muhammad Rifaldo Al Magribi Nada Tsawaabul Khair Nazir, Alwis Norhiza, Fitra Lestari Novriyanto Novriyanto Nurul Ikhsan Okfalisa Okfalisa Pizaini Pizaini Prima Yohana Rahmah Miya Juwita Raja Indra Ramoza Ramadhani, Astrid Risfi Ayu Sandika Robbi Nanda Robby Azhar Salmiyati Salmiyati Sardi, Hajra Satria Bumartaduri Sayyid Muhammad Habib Siti Ramadhani Siti Ramadhani Siti Ramadhani Surya Agustian Suwanto Sanjaya Syafira, Fadhilah Syafria, Fadhillah Syahbudin Hamwar Syaputra, Muhammad Dwiky Teddie Darmizal Umam, Isnaini Hadiyul Vusuvangat, Imam Wan Muhammad Hanif Wulandari, Fitri Yayuk Wulandari yelfi Vitriani Yelfi Yelfi Yola, Melfa Yusra Yusra Yusra Yusra, - Yusra, Yusra