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All Journal snimed Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Prosiding SNATIF Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Sistem Cerdas Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Bumigora Information Technology (BITe) ABIDUMASY : Jurnal Pengabdian kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Computer Science and Information Technology (CoSciTech) J-Intech (Journal of Information and Technology) Jurnal Sains dan Teknologi MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Knowbase : International Journal of Knowledge in Database Journal of Artificial Intelligence and Digital Business Bakti Sekawan : Jurnal Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Jurnal Pengabdian Sosial Journal of Management, Entrepreneurship and Tourism JUAN: Jurnal Pengabdian Nusantara Jurnal Pengabdian Pada Masyarakat IPTEKS Prosiding Seminar Nasional CORISINDO
Neny Sulistianingsih
Bumigora University
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Enhancing Predictive Models: An In-depth Analysis of Feature Selection Techniques Coupled with Boosting Algorithms Neny Sulistianingsih; Galih Hendro Martono
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3788

Abstract

This research addresses the critical need to enhance predictive models for fetal health classification using Cardiotocography (CTG) data. The literature review underscores challenges in imbalanced labels, feature selection, and efficient data handling. This paper aims to enhance predictive models for fetal health classification using Cardiotocography (CTG) data by addressing challenges related to imbalanced labels, feature selection, and efficient data handling. The study uses Recursive Feature Elimination (RFE) and boosting algorithms (XGBoost, AdaBoost, LightGBM, CATBoost, and Histogram-Based Boosting) to refine model performance. The results reveal notable variations in precision, Recall, F1-Score, accuracy, and AUC across different algorithms and RFE applications. Notably, Random Forest with XGBoost exhibits superior performance in precision (0.940), Recall (0.890), F1-Score (0.920), accuracy (0.950), and AUC (0.960). Conversely, Logistic Regression with AdaBoost demonstrates lower performance. The absence of RFE also impacts model effectiveness. In conclusion, the study successfully employs RFE and boosting algorithms to enhance fetal health classification models, contributing valuable insights for improved prenatal diagnosis.
Deep Learning-Based Classification of Fetal Head Abnormalities from Ultrasound Images Using EfficientNet-B3 Galih Hendro Martono; Neny Sulistianingsih
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.580

Abstract

Fetal brain abnormalities represent a critical concern in prenatal diagnostics due to their significant impact on neonatal survival and neurological development. Conventional ultrasound (USG) screening relies heavily on expert interpretation, which can be time-consuming and prone to subjectivity. To overcome this constraint, this research develops an automated classification approach employing deep learning techniques to recognize fetal head abnormalities captured through ultrasound scans. The dataset, obtained from a publicly available Kaggle repository, comprises fourteen diagnostic categories, including Arnold Chiari Malformation, Arachnoid Cyst, Cerebellar Hypoplasia, Holoprosencephaly, and Ventriculomegaly variations, among others. Each ultrasound image was subjected to a series of preprocessing operations, such as resizing to 224×224 pixels, applying normalization, and performing data augmentation, to enrich feature variability and strengthen the model’s generalization capability. A pretrained EfficientNet-B3 architecture was fine-tuned for multi-class classification, with the fully connected layer adapted to predict fourteen distinct abnormality classes. Model training was conducted for ten epochs using the Adam optimizer and cross-entropy loss function, with performance evaluated via training loss and validation accuracy metrics. The results demonstrate rapid convergence, with training loss decreasing from 1.7055 in the first epoch to 0.0387 in the final epoch. Concurrently, validation accuracy improved from 79.60% to a peak of 91.37%, indicating strong generalization capability. The consistent upward trend in accuracy and the downward trend in loss confirm the model’s stability and effective learning behavior. Overall, the proposed EfficientNet-B3–based approach achieves high accuracy and robustness, highlighting its potential as an assistive tool for automated prenatal diagnosis of fetal brain abnormalities
A Locally Grounded Retrieval-Augmented LLM-Based Chatbot for Bilingual Stunting Prevention Consultation among Health Cadres in Indonesia Tanwir, Tanwir; Hidjah, Khasnur; Susilowati, Dyah; Anggrawan, Anthony; Sulistianingsih, Neny
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5459

Abstract

Stunting remains a major public health challenge in Indonesia, affecting 21.6% of children under five nationally and 18.34% in Nusa Tenggara Barat (NTB), which strains the capacity of health cadres to deliver timely and accurate nutrition education. This study aims to develop a consultation chatbot by integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to provide context-aware stunting prevention guidance. A total of 45 journal articles and 7 books were curated to construct 7,642 question–answer pairs using a RAG-based pipeline. Text preprocessing involved segmentation, embedding, and Byte Pair Encoding tokenization, followed by fine-tuning a LLaMA 3 model on an NVIDIA L4 GPU. Model performance was evaluated using ROUGE and BERTScore metrics, complemented by a small pilot usability assessment. The RAG-integrated model achieved a ROUGE-1 score of 81.03% and a BERTScore F1 of 93.48%, consistently outperforming baseline models. These findings demonstrate the potential of RAG-enhanced LLMs to support scalable and accessible health informatics solutions for empowering health cadres in resource-limited and rural settings.
Impementasi Algoritma GRU Untuk Trading Strategy pada Cryptocurrency Berbasis Web I Gede Bayu Balawa Tangub; Neny Sulistianingsih; Tomi Tri Sujaka
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.10129

Abstract

Perkembangan investasi cryptocurrency, khususnya Ethereum (ETH), terus meningkat seiring dengan bertambahnya jumlah investor aset digital. Namun, tingginya volatilitas harga cryptocurrency menyebabkan proses pengambilan keputusan trading menjadi lebih kompleks dan berisiko. Oleh karena itu, diperlukan metode prediksi yang mampu menghasilkan informasi harga secara akurat untuk mendukung pengambilan keputusan investasi. Penelitian ini bertujuan untuk membandingkan performa algoritma Gated Recurrent Unit (GRU) dan Bidirectional Gated Recurrent Unit (Bi-GRU) dalam memprediksi harga penutupan Ethereum serta mengimplementasikan model terbaik ke dalam sistem trading strategy berbasis web. Metode penelitian menggunakan pendekatan Cross Industry Standard Process for Data Mining (CRISP-DM) yang meliputi tahapan business understanding, data understanding, data preparation, modeling, evaluation, dan deployment. Dataset yang digunakan berupa data historis harian ETH-USD yang diperoleh dari Yahoo Finance pada periode 9 November 2016 hingga 16 Januari 2026. Proses penelitian mencakup prapemrosesan data, pelatihan model, evaluasi performa menggunakan RMSE, MAE, MSE, R², dan Explained Variance, serta implementasi sistem berbasis web. Hasil penelitian menunjukkan bahwa model GRU memberikan performa yang lebih baik dibandingkan Bi-GRU dengan nilai RMSE sebesar 150,99, MAE sebesar 117,70, MSE sebesar 22.798,51, R² sebesar 0,96, dan Explained Variance sebesar 0,98. Hasil prediksi kemudian diintegrasikan ke dalam strategi trading berbasis aturan yang menghasilkan sinyal buy, sell, dan hold. Sistem berhasil diimplementasikan menggunakan Vercel dan Hugging Face Spaces. Hasil penelitian menunjukkan bahwa algoritma GRU efektif untuk prediksi harga Ethereum dan berpotensi mendukung pengambilan keputusan trading berbasis data secara lebih objektif.
Perbandingan Kinerja Random Forest Regression dan Support Vector Regression dalam Forecasting Harga Saham Indeks LQ45 Githa Alfiansyah; Neny Sulistianingsih; I Nyoman Switrayana
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.10759

Abstract

Forecasting harga saham merupakan tantangan yang kompleks karena sifat pergerakan data yang fluktuatif dan tidak linear, terutama pada saham indeks LQ45. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Random Forest Regression dan Support Vector Regression dalam meramalkan harga penutupan saham BBRI, TLKM, dan ANTM. Pemodelan dilakukan menggunakan data historis periode 1 Januari 2020 hingga 30 april 2026, dengan memanfaatkan ekstraksi fitur lag 1 sampai 5 dan Moving Average MA5, MA10. Evaluasi kinerja model dilakukan dalam dua skenario, yaitu parameter default dan tuning parameter secara manual, serta diukur menggunakan metrik Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa Support Vector Regression memiliki kinerja dan ketangguhan (robust) yang lebih unggul dibandingkan Random Forest Regression. Proses optimasi menunjukkan bahwa kernel linear terbukti menjadi yang terbaik untuk Support Vector Regression dalam peramalan harga saham karena konsisten menghasilkan tingkat kesalahan minimum. Support Vector Regression dengan kernel linear mencapai nilai evaluasi MAE 50,25; RMSE 66,07; dan MAPE 1,36% pada saham BBRI, serta MAE 65,44; RMSE 89,25; dan MAPE 1,92% pada saham TLKM. Keunggulan Support Vector Regression terlihat sangat signifikan pada saham ANTM yang memiliki volatilitas tinggi, di mana Support Vector Regression sukses menekan kesalahan hingga MAPE 2,65%, sedangkan Random Forest Regression gagal mengekstrapolasi tren lonjakan harga. Dengan demikian, Support Vector Regression berkonfigurasi kernel linear dinobatkan sebagai model peramalan yang paling direkomendasikan karena akurat dan stabil dalam merespons fluktuasi pasar modal.
Identification of top influence users in disseminating information on the 2024 Indonesian National Election Neny Sulistianingsih; Galih Hendro Martono
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 14 No. 1 (2024): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v14i1.25-32

Abstract

Social media has a vital role in general elections in Indonesia because social media is one of the platforms used by presidential candidates for campaigns to gain public support. General elections in Indonesia occur every five years. Many tweets talk about presidential candidates approaching the national election period. Not least, some buzzers deliberately use Twitter to carry out propaganda against a candidate or to bring down other presidential candidates with their opinions because information can spread widely and quickly on Twitter. Based on this, it is necessary to identify influential users in disseminating information related to the 2024 National Election, especially on Twitter. Various centrality methods were used in this study to identify influence users in sharing information about the 2024 National Election such us Degree Centrality, Closeness Centrality, Harmonic Centrality, Eigenvector Centrality, and Load Centrality. For the evaluation in this study, the results of each method were compared to one another to measure the similarity and correlation between the ranking lists of users who were influential in disseminating information about the 2024 National Election.
Model Deteksi Serangan Jaringan Menggunakan Machine Learning Dengan Teknik Ensemble Learning Lauw, christopher Michael; Advaita Hary, Adex; Anggrawan, Anthony; Syahrir, Moch.; Sulistianingsih, Neny
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol. 15 No. 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3369

Abstract

Stock is one of the most popular investment instruments due to its potential to generate substantial returns. However, the high volatility of stock prices requires investors to employ accurate prediction models to support investment decision-making. This study aims to compare the performance of the Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods in predicting the stock price of PT Gudang Garam Tbk using historical data enriched with technical indicators. The study adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which consists of six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The prediction models were developed using historical stock price data enriched with technical indicators and evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The experimental results demonstrate that the ANN model outperformed the SVR model, achieving an MSE of 2923.86, RMSE of 1709.93, MAE of 1294.76, MAPE of 8.38%, and an R² of 0.68, while the SVR model obtained an MSE of 5211.06, RMSE of 2284.57, MAE of 2126.84, MAPE of 12.73%, and an R² of 0.42. Furthermore, the 240-day forecasting results indicate that the ANN model projects an upward (bullish) trend, whereas the SVR model predicts a relatively stable (sideways) trend. These findings indicate that the Artificial Neural Network (ANN) is more effective than Support Vector Regression (SVR) for predicting the stock price of PT Gudang Garam Tbk, as it produces lower prediction errors and demonstrates superior predictive performance. Keyword: Stock Price Prediction, Artificial Neural Network, Support Vector Regression, CRISP-DM. Abstrak Saham merupakan salah satu instrumen investasi yang banyak diminati karena berpotensi memberikan keuntungan yang tinggi. Namun, tingginya volatilitas harga saham menyebabkan investor memerlukan model prediksi yang akurat sebagai dasar pengambilan keputusan investasi. Penelitian ini bertujuan untuk membandingkan kinerja metode Artificial Neural Network (ANN) dan Support Vector Regression (SVR) dalam memprediksi harga saham PT Gudang Garam Tbk menggunakan data historis yang diperkaya dengan indikator teknikal. Penelitian ini menerapkan metodologi Cross-Industry Standard Process for Data Mining (CRISP-DM) yang meliputi tahapan business understanding, data understanding, data preparation, modeling, evaluation, dan deployment. Model dibangun menggunakan data historis harga saham yang diperkaya dengan indikator teknikal, kemudian dievaluasi menggunakan metrik Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa model ANN memberikan performa yang lebih baik dibandingkan SVR dengan nilai MSE sebesar 2923,86, RMSE sebesar 1709,93, MAE sebesar 1294,76, MAPE sebesar 8,38%, dan R² sebesar 0,68, sedangkan model SVR memperoleh nilai MSE sebesar 5211,06, RMSE sebesar 2284,57, MAE sebesar 2126,84, MAPE sebesar 12,73%, dan R² sebesar 0,42. Pada prediksi jangka panjang selama 240 hari, model ANN memproyeksikan tren harga yang meningkat (bullish), sedangkan model SVR menghasilkan tren yang relatif stabil (sideways). Berdasarkan hasil tersebut, dapat disimpulkan bahwa metode Artificial Neural Network (ANN) lebih efektif dibandingkan Support Vector Regression (SVR) dalam memprediksi harga saham PT Gudang Garam Tbk karena mampu menghasilkan tingkat kesalahan yang lebih rendah dan kemampuan prediksi yang lebih baik. Kata kunci: Prediksi harga saham; Artificial Neural Network; Support Vector Regression; CRISP-DM.
Co-Authors Abdul Muhid Advaita Hary, Adex Afrian, M.Alawi Agus Syahid Alfian Nurlifa Anthony Anggrawan Christine Eirene christofer satria Dewi, Ni Putu Sinta Dian Syafitri Dyah Susilowati Galih Hendro Martono Githa Alfiansyah Hairani Hairani Hammad, Rifqi Hasbullah Hasbullah Hasbullah Hasbullah Hidayat, Fadila Ananda Kartika I Gede Bayu Balawa Tangub I Nyoman Switrayana Irving Vitra Paputungan Kariyam - Khasnur Hidjah lauw, Christopher Michael Lilik Widyawati Lukmana, Aldi Mayasari, Novyta Indah Nafiani Nur Khamidah Ni Ketut Putri Nila Sudewi Ni Putu Sinta Dewi Noviansyah Noviansyah, Noviansyah Nurramadhan, Aliva Putradi, Angga Ria Rismayati Rismayanti, Ria Rismayati, Ria Setyawan, Ari Shofwan, Ari Sirojul Hadi Sri Kusumadewi Sutarman Sutarman Syahrir, Moch. Tanwir, Tanwir Tomi Tri Sujaka Triwijoyo, Bambang Krismono Uswatun Hasanah Vira Megasari Haqni