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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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Implementasi Metode RBMT dalam Penerjemahan Bahasa Indonesia ke Bahasa Makassar Wan Muhammad Hanif; 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.
Analisis Kinerja Recursive Feature Elimination pada Support Vector Machine untuk Klasifikasi Penyakit Stroke pada Data Tidak Seimbang Faridatul Jannah; Siska Kurnia Gusti; Elin Haerani; Teddie Darmizal
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1147

Abstract

Stroke is a non-communicable disease with high mortality and disability rates, necessitating a classification approach that can facilitate more effective detection. Class imbalance in stroke datasets causes classification models to be biased toward the majority class, resulting in suboptimal classification performance. This study aims to analyze the performance of Recursive Feature Elimination (RFE) in a Support Vector Machine (SVM) model with data imbalance handling using Adaptive Synthetic Sampling (ADASYN) in stroke classification. The dataset used is a secondary dataset from Kaggle consisting of 5109 data points after the preprocessing stage. The modeling process was conducted by testing various data split ratios as well as combinations of kernels and SVM parameters using a 5-fold cross-validation approach. The results show that the best model was obtained with an 80:20 split ratio, a polynomial kernel, and a C parameter of 0.1, yielding an accuracy of 0.75, precision of 0.14, recall of 0.82, an F1-score of 0.24, and an AUC of 0.8245. The application of RFE resulted in improved model performance compared to without RFE, although the magnitude of the improvement was relatively small. The still low precision value indicates that the model still produces many false positives, so the classification challenge on the stroke dataset has not been fully resolved. On the other hand, an AUC value of 0.8245 indicates that the model performs reasonably well in distinguishing between the two classes overall, although its application in a clinical context still requires further refinement.
Implementasi Model Long Short Term Memory (LSTM) dalam Prediksi Harga Saham Juliandi Kurniansyah; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2005

Abstract

Stock market investment is gaining popularity, although predicting stock price fluctuations remains challenging. Accurate stock prediction models can assist investors in decision-making. In this research, a Long Short-Term Memory (LSTM) model was employed to make predictions regarding the stock prices of BBCA based on daily historical data from January 1 2015 to January 1 2025. The data was gathered from the Yahoo Finance website, utilizing only the closing price ('close') variable. The research process included data pre-processing, Min-Max normalization, LSTM modeling with varying timesteps (30, 60, 90 days), and evaluation of prediction results. The LSTM model was built with two LSTM layers, a dropout layer, and a final dense layer, and its training involved the application of the mean_squared_error loss function and Adam optimizer. Evaluation results showed that the model configuration with 60 timesteps achieved optimal performance with a RMSE of 114.17, MAPE percentage of 0.96%, and an R-Squared of 0.98, indicating highly accurate and reliable predictions. This study demonstrated that LSTM is an effective model for stock price prediction based on time series data.
Clustering of Halal MSME Aid Recipients: Uncovering Patterns and Characteristics Using the K-Medoids Method yelfi Vitriani; Siska Kurnia Gusti
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

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 Muhammad Hafiz; Elvia Budianita; Alwis Nazir; Siska Kurnia Gusti
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 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