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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Prosiding SNATIKA Vol 01 (2011) Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Building of Informatics, Technology and Science E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Infotech: Journal of Technology Information Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Abdimas Indonesia Journal Of Artificial Intelligence And Software Engineering Masyarakat Mandiri: Jurnal Pengabdian Dan Pembangunan Lokal Jurnal Sistem Informasi dan Teknologi Informasi
Andrianingsih
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Penerapan Web Pemesanan Usaha Ketering Dapoer Bu Yunus Dengan Metode Agile Kusuma, Rega Andhika; Andrianingsih, Andrianingsih
e-Jurnal JUSITI (Jurnal Sistem Informasi dan Teknologi Informasi) Vol. 13 No. 1 (2024): e-Jurnal JUSITI
Publisher : Universitas Dipa Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36774/jusiti.v13i1.1562

Abstract

Memasuki era revolusi ke-4 dimana semua aspek beralih ke digitalisasi, banyak pelaku usaha makanan dalam pengelolaan pemesanan masih secara konvensional, seperti proses pesan dan pembayaran. Hal tersebut dapat menyebabkan kesalahan dalam pencatatan pesanan. Oleh karena itu, perlu dirancangnya sistem pemesanan berbasis web yang efektif dan mempermudah proses pemesanan. Tujuan dari penelitian ini yaitu untuk meningkatkan efisiensi pada sistem pemesanan katering berbasis web dengan metode pengembangan Agile dengan alur kerja Kanban. Metode ini menekankan kecepatan, keluwesan, dan interaksi intens, dengan menghasilkan perangkat lunak berkualitas. Penelitian ini akan menggunakan metode agile untuk mengembangkan sistem pemesanan makanan pada katering berbasis web untuk mempermudah pelanggan dalam melakukan pemesanan dan membantu penjual dalam mengelola pesanan. Hasil penelitian dengan sistem pengujian aplikasi menggunakan metode load testing dengan 100 pengguna mengakses 10 halaman berbeda pada ramp-up 1 detik dan pengulangan 10 kali memiliki 10000 sampel yang diuji, dengan hasil rata-rata response time 437ms, throughput rata-rata 5,5/sec dan error 0%, yang menunjukan tingkat performa aplikasi yang baik.
Machine Learning Analysis of Jakarta Bay Water Quality: Comparing Models Savira, Aura; Andrianingsih, Andrianingsih
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15540

Abstract

Jakarta Bay experiences persistent anthropogenic pressures that produce spatially heterogeneous water-quality conditions. This study develops a regulation-aligned, explainable classification framework using a 2024 in-situ dataset collected at 53 stations across two sampling periods (March and August). After preprocessing—including unit harmonization, outlier screening, missing-value imputation, and treatment of below-detection-limit measurements—the dataset yielded 104 complete samples classified into Good (n=46), Lightly Polluted (n=28), and Moderately Polluted (n=34) categories based on KEPMEN LH No. 51/2004. Three ensemble algorithms (LightGBM, CatBoost, and Random Forest) were evaluated using stratified cross-validation to maintain class balance and prevent spatial leakage. CatBoost achieved the best overall performance (Accuracy = 0.8338; F1 = 0.8257), followed by Random Forest, while LightGBM showed the highest variability across folds. Class-level metrics indicate that CatBoost produced the most balanced predictions, particularly for the borderline Lightly Polluted class. SHAP analysis identified turbidity/TSS, nutrients, dissolved oxygen, salinity, and spatial gradients as dominant predictors, enabling transparent interpretation of model decisions. The resulting framework provides a reproducible and operationally deployable approach for rapid screening, hotspot detection, and decision support in Jakarta Bay’s water-quality management.
Heart Disease Classification Using Optimised XGBoost and Random Forest with SHAP Explanations Hutagalung, Pancar Hizkia; Andrianingsih, Andrianingsih
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15544

Abstract

Heart disease remains one of the leading causes of global morbidity, creating a need for accurate and interpretable computational tools to support early diagnosis. However, many existing studies on the Cleveland Heart Disease dataset rely on limited validation protocols, apply only a single hyperparameter optimisation strategy, or provide narrow explainability analyses, which can lead to optimistic performance estimates and inconsistent clinical insight. This study addresses these gaps by proposing a classification-based prediction framework that evaluates Random Forest and XGBoost for binary heart-disease classification under three hyperparameter optimisation strategies random search, Bayesian optimisation, and particle swarm optimisation (PSO) within a nested, anti-leakage cross-validation design, while SHAP is employed to analyse model interpretability across the best-performing configurations. The experimental results show that the ensemble classifiers achieve strong and consistent performance, with ROC–AUC values ranging from 0.8908 to 0.9089 across all scenarios; Random Forest optimised with PSO obtained the highest ROC–AUC (0.9089 ± 0.0146) and F1-score (0.8188 ± 0.0206), whereas XGBoost with Bayesian optimisation reached comparable performance without statistically significant differences. SHAP analyses identified oldpeak, ca, thal, cp, thalach, and exang as the most influential features, in line with established clinical indicators of myocardial ischemia and perfusion abnormalities. These findings indicate that combining tree-based ensemble classifiers with systematic hyperparameter optimisation and SHAP-based interpretability can enhance the reliability and transparency of heart-disease classification on the Cleveland dataset, while highlighting the need for further validation on contemporary, multi-centre clinical data.
Comparing XGBoost and LightGBM for Optimizing Health Content Categories Oktaviana, Nanda; Andrianingsih, Andrianingsih
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15545

Abstract

Indonesia’s social media platforms contain large amounts of unverified health information. Research on Indonesian health-text mining still rarely focuses on disease-based classification, leaving a gap compared with studies that only address sentiment or general topic categorization. This study proposes a multi-class classification approach that uses IndoBERT embeddings combined with gradient-boosting classifiers (XGBoost and LightGBM) to categorize tweets into diabetes, hypertension, and heart disease. The dataset comprises 4,075 tweets collected from platform X (Twitter). Preprocessing involves text cleaning, anonymization, normalization, and the extraction of 768-dimensional IndoBERT embeddings. Experiments are conducted in Google Colab (Intel Xeon CPU, 13 GB RAM, optional NVIDIA T4 GPU) using stratified five-fold cross-validation.The best results are obtained by the IndoBERT × LightGBM pipeline, which achieves an accuracy of 0.8526 and a macro-averaged F1-score of 0.8527, outperforming the IndoBERT × XGBoost model (accuracy 0.8325 and macro F1-score 0.8326). Feature-importance analysis shows that contextual terms related to blood sugar, the heart, and blood pressure strongly influence the predictions. Overall, the proposed method provides an effective baseline for monitoring health-related text and supporting disease-oriented analytics in Indonesian-language social media.
Co-Authors Agung Triayudi Ahmad Taufik Aldisa, Rima Tamara Aldjawad, Mohamad Alhadi, Alhadi Alhakim, Rahmat Ambi Muhammad Dzuhri, Ambi Muhammad Annisa Annisa Asrul Sani Buhori, Kiki Burhani, Hanif Rahman Candra, Hansen Chafidin, Afiyan Nur Dhieka Avrilia Lantana Elok Ainur Latif Endah Tri Esti Handayani Erlinda, Mely Erliza Hambali Fakrurrozi, Fakrurrozi Fauziah Fauziah Firzatullah, Raden Muhamad Hindarto, Djarot Hutagalung, Pancar Hizkia Ina Agustina Indra Lukmana Iskandar Fitri, Iskandar Kaeren, Kaeren Kusuma, Rega Andhika Maharani, Anggita Putri Mochamad Hariadi Muhammad Nurdin Muhammad Taufiq Muhammad, Taufik Mutia Pamikatsih Nur Hayati oktaviana, nanda Pratama, Wildan Ikbar Putra, Gifari Yudiya Ramadhan, Jonathan Aditya Ratih Titi Komalasari Riyantoro Riyantoro Sari Ningsih Savira, Aura Septi Andryana setiawan, Danis Shalihati, Ira Diana Siti Aisyah Soepriyono, Gatot Subkhi, Akhmad Yunus Suparman, Kodim Susanto, Arman Rama Syaiful Syaiful Wahyuddin, M Iwan Wahyuddin, Mohammad Iwan WIJANARKO, SIGIT Wiratama Putra, Tobby Zuraidah, Arika