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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 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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Penerapan Algortitma C4.5 untuk Klasifikasi Sentimen Masyarakat terhadap #RUUKUHP pada Twitter Vusuvangat, Imam; Kurnia Gusti, Siska; Syafira, Fadhilah; Novriyanto, Novriyanto; Insani, Fitri
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 4 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Social media, especially Twitter, has developed into an important tool for people to share their opinions and feelings widely. Users often use hashtags to share messages related to certain topics. Some of the issues that lead to the need for sentiment analysis of the Draft Criminal Code are social impact, Public disapproval, Potential legal uncertainty, Potential abuse, Support and criticism. By conducting a sentiment analysis of the draft Penal Code, the government and policymakers can better understand the views of the public, identify possible problems and address them, and make necessary improvements or clarifications to the draft law. This can help ensure that the draft Penal Code has greater public support and adheres to good legal principles. The classification of public responses to this hashtag provides a significant snapshot of public attitudes and perspectives. This study aims to classify public sentiment towards the RUUKUHP hashtag on the Twitter platform using the C4.5 algorithm. This study uses a collection of tweets with the hashtag RUUKUHP which are manually categorized into two and three sentiment categories, namely positive, negative and positive, negative and neutral. In this study, data preprocessing is carried out before training the model which includes removing links, special characters, removing stopwords, and word tokenization. Furthermore, this research uses text representation methods such as TF-IDF to extract features from the tweet text and convert them into numerical vectors used by the C4.5 algorithm. After training the classification model using the C4.5 algorithm with the classified dataset, it evaluates the performance of the model with the metrics of accuracy, recall, precision, and F1 score. Experimental results using 2 categories of Negative and Positive show that the model applied with the C4.5 algorithm achieved an accuracy of 96.6% with a recall of 96.6%, a percision of 97.1% and an F1 score of 96.8. And experiments using 3 categories of Negative, Positive and Neutral achieved an accuracy of 67%, a recall of 67%, a precision of 65%, and an F1 score of 66%. Thus it can be concluded that the results of the RUUKUHP hashtag sentiment classification with 2 class predictions are more relevant than 3 sentiment class predictions with a value reaching 96.6%.
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.
Klasifikasi Tingkat Keberhasilan Produksi Ayam Broiler di Riau Menggunakan Algoritma Naïve Bayes Hamwar, Syahbudin; Nazir, Alwis; Gusti, Siska Kurnia; Iskandar, Iwan; Insani, Fitri
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%.
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.
Implementasi Metode RBMT dalam Penerjemahan Bahasa Indonesia ke Bahasa Makassar Hanif, Wan Muhammad; Yusra, Yusra; Muhammad Fikry; Febi Yanto; 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.v6i1.935

Abstract

?This research was conducted to address the limited availability of linguistic resources for regional languages, particularly Makassar Language, which does not yet have adequate automatic translation support. The main problem addressed in this study is the absence of a reliable automatic translation system for Makassar Language. The objective of this research is to apply a rule-based translation method to translate text from Indonesian into Makassar Language. This study focuses on the implementation of the Rule-Based Machine Translation (RBMT) method for translating Indonesian text into Makassar Language using the Python programming language. The RBMT implementation involves tokenization, morphological analysis, vocabulary matching, and the application of grammatical rules, including the identification of prefixes and suffixes. The data used consist of a bilingual dictionary compiled from various sources and a set of test sentences representing everyday sentence structures. Translation evaluation was carried out using the Word Error Rate (WER) method, yielding a result of 0.289, and the Character Error Rate (CER) method, with a result of 0.21, which fall into the “Good” category based on the evaluation scale. The main findings indicate that the application of the RBMT method is capable of producing reasonably accurate translations at both the word and character levels. These findings demonstrate that a rule-based approach can be effectively applied to regional languages with limited digital data and provide an initial overview of the potential use of rule-based methods to support the development and preservation of regional languages.
Clustering of Halal MSME Aid Recipients: Uncovering Patterns and Characteristics Using the K-Medoids Method Vitriani, yelfi; Gusti, Siska Kurnia
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i2.38634

Abstract

The rapid growth of the halal industry has strengthened the strategic role of Micro, Small, and Medium Enterprises (MSMEs) in meeting market expansion. However, the absence of structured insights regarding the characteristics and patterns of halal MSME aid recipients has hindered the formulation of effective and targeted support programs. This study aims to identify the clustering patterns of halal MSME beneficiaries in Indonesia using the K-Medoids algorithm optimized with Principal Component Analysis (PCA). A total of 129 MSME datasets were collected through validated questionnaires consisting of demographic variables, aid history, business performance, and operational challenges. Preprocessing included data cleaning, transformation, and dimensionality reduction using PCA. The optimal PCA dimension was determined as two components based on the Davies-Bouldin Index (0.1737). K-Medoids clustering produced three optimal clusters validated using Silhouette (0.4602), Davies-Bouldin Index (0.7861), and Elbow Method (K=3). Each cluster shows distinctive characteristics in income range, business legality, type of aid received, challenges, and performance outcomes. The novelty of this research lies in the application of PCA-optimized K-Medoids for halal MSME segmentation, providing insightful foundations for evidence-based policymaking.
KLASIFIKASI MINAT BACA MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Hafiz, Muhammad; Budianita, Elvia; Nazir, Alwis; Gusti, Siska Kurnia
Journal of Economic, Bussines and Accounting (COSTING) Vol. 9 No. 1 (2026): COSTING : Journal of Economic, Bussines and Accounting
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/gcqhr236

Abstract

Minat baca merupakan faktor penting dalam mendukung keberhasilan akademik siswa, namun pengukurannya masih sering dilakukan secara subjektif. Penelitian ini bertujuan untuk mengklasifikasikan tingkat minat baca siswa MTsN 1 Payakumbuh menggunakan metode Naïve Bayes Classifier. Atribut yang digunakan sebagai input berupa 20 butir pernyataan kuesioner skala Likert yang merepresentasikan kebiasaan, frekuensi, motivasi, serta preferensi membaca siswa. Data penelitian diperoleh dari 911 responden yang dikelompokkan ke dalam tiga kelas tingkat minat baca, yaitu tinggi (329 data), sedang (501 data), dan rendah (81 data). Pengujian model dilakukan menggunakan tiga skema pembagian data latih dan data uji, yaitu 90:10, 80:20, dan 70:30. Evaluasi performa model menggunakan confusion matrix menunjukkan bahwa skema 90:10 menghasilkan akurasi sebesar 96,74%, skema 80:20 sebesar 97,81%, dan skema 70:30 sebesar 98,18%. Hasil tersebut menunjukkan bahwa metode Naïve Bayes Classifier memiliki performa yang sangat baik dan konsisten dalam mengklasifikasikan tingkat minat baca siswa berdasarkan data kuesioner.
Co-Authors Abdul Wahid Abdullah Abdullah Abdullah, Said Noor Abdussalam Al Masykur Adi Mustofa Al Rasyid, Nabila Alfaiza, Raihan Zia Alfin Hernandes Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Amelia, Felina Anggi Vasella Azhima, Mohd Baehaqi Beni Basuki 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 Faska, Ridho Mahardika Febi Yanto Fitri Insani Fitri Insani Fitri Wulandari Fitri, Anisa Gusti, Gogor Putra Hafi Puja Hamwar, Syahbudin Hanif, Wan Muhammad Iis Afrianty Iis Afrianty Iqbal Salim Thalib Irsyad (Scopus ID: 57204261647), Muhammad Iwan Iskandar Jasril Jasril Jasril Jasril Khair, Nada Tsawaabul Kurniansyah, Juliandi Lestari Handayani M Wandi Dwi Wirawan Maemonah, Maemonah Morina Lisa Pura Muhammad Affandes Muhammad Fauzan Muhammad Fikry Muhammad Hafiz Muhammad Irsyad Muhammad Khairy Dzaky Muhammad Rifaldo Al Magribi 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 Sardi, Hajra Satria Bumartaduri Sayyid Muhammad Habib Siti Ramadhani Siti Ramadhani Siti Ramadhani Surya Agustian Suwanto Sanjaya Syafira, Fadhilah Syafria, Fadhillah Syaputra, Muhammad Dwiky Umam, Isnaini Hadiyul Vitriani, Yelfi Vusuvangat, Imam Wulandari, Fitri Yayuk Wulandari Yelfi Yelfi Yola, Melfa Yusra Yusra, - Yusra, Yusra