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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi Prosiding SNATIF Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IKRA-ITH Informatika : Jurnal Komputer dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian UNDIKMA Jurnal Pengabdian Literasi Digital Indonesia Indonesian Journal of Health Information Management Services (IJHIMS) Community: Jurnal Pengabdian Pada Masyarakat Prosiding Seminar Riset Mahasiswa Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Green Engineering: Journal of Engineering and Applied Science
Fahmi Amiq
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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 Mathematics Nursing Public Health Social Sciences Other

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Proboboost: A Hybrid Model for Sentiment Analysis of Kitabisa Reviews Prasetya, Rakan Shafy; Fahmi, Amiq; Sulistyono, MY Teguh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11138

Abstract

The rapid advancement of digital technology has significantly transformed public behavior in social activities, particularly in online donations and zakat payments. The Kitabisa application was selected in this study not only for its popularity but also due to its high user engagement and large volume of reviews on the Google Play Store, making it an ideal representation of public trust in Indonesia’s digital philanthropy ecosystem. This research aims to analyze user sentiment toward the Kitabisa application using a hybrid Proboboost model, which combines Multinomial Naive Bayes (MNB) and Gradient Boosting Classifier through a soft voting mechanism. The model is designed to address class imbalance and improve accuracy in short-text sentiment analysis for the Indonesian language. The study employed preprocessing techniques including case folding, text cleaning, stopword removal, and stemming using the Sastrawi algorithm. Feature extraction was performed using TF-IDF, with an 80:20 train-test split and 5-fold cross-validation to ensure model reliability. Experimental results indicate that the Proboboost model achieved an accuracy of 89.51% and an F1-score of 87.4%, outperforming the Naive Bayes baseline with 87.98% accuracy. The sentiment distribution demonstrates a dominance of positive sentiment (87.24%), followed by negative (8.53%) and neutral (4.23%) reviews. These findings suggest that users generally express satisfaction and trust toward the Kitabisa platform. The results also confirm that the hybrid Proboboost model effectively balances classification performance between majority and minority sentiment classes, offering deeper insights into user perceptions of digital philanthropic services.
Optimized LSTM with TSCV for Forecasting Indonesian Bank Stocks Salsabila, Rizka Mars; Fahmi, Amiq; Al Zami, Farrikh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11314

Abstract

Volatility in financial markets presents complex forecasting challenges for investors, particularly within emerging economies such as Indonesia. This study proposes an optimized Long Short-Term Memory (LSTM) model for forecasting the stock prices of five significant Indonesian banks: BBCA, BBRI, BMRI, BBNI, and BBTN, utilizing daily OHLCV data (Open, High, Low, Close, Volume) and technical indicators from 2020 to 2025. The dataset comprises over 6,000 daily records, segmented using a sliding window approach to preserve temporal structure and enhance learning efficiency. Concurrently, the model architecture comprising dual LSTM layers with dropout regularization was refined through systematic hyperparameter tuning to enhance predictive performance. Model evaluation employed 5-fold Time Series Cross-Validation (TSCV), a sequential validation technique that mitigates data leakage and explicitly overcomes the limitations of conventional k-fold methods by preserving chronological integrity. Performance metrics included MSE, RMSE, MAE, R², and MAPE. The experiment results demonstrate the model’s robustness in capturing long-term dependencies within financial time series. BBCA and BMRI achieved superior accuracy (R² > 0.95), with BBCA recording the lowest MAPE of 2.34%. Despite market fluctuations, the model maintained consistent reliability across all test folds. This study overcomes a methodological limitation by integrating LSTM with TSCV in expanding markets, offering actionable insights for investors, analysts, and policymakers, and serving as a reference for adaptive AI-based, more informed forecasting tools. Moreover, the proposed framework holds promise for broader application across other financial sectors and regional markets with similar volatility characteristics.
PENGUATAN KAPASITAS BIDAN DAN KADER PUSKESMAS DUREN: TRAINING OF TRAINER MODEL PENDAMPINGAN DIGITAL PERAWATAN KEHAMILAN-NIFAS Wulandari, Respati; Wibowo, Syifa Sofia; Apriyanti, Apriyanti; Fahmi, Amiq; Laurensius Tokan, Geraldinho; Yumna Huwaida, Imtiyaz
Indonesian Journal of Health Information Management Services Vol. 5 No. 2 (2025): Indonesian Journal of Health Information Management Services (IJHIMS)
Publisher : APTIRMIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33560/ijhims.v5i2.148

Abstract

The phenomenon of teenage pregnancy has serious health, social, and economic impacts. Globally, 55% of pregnancies among 15-19 year olds result in unsafe abortions, including in Indonesia. Based on interviews with the midwife coordinator, BOK funds are only sufficient to hold classes for 15 pregnant women. There is a disparity between the number of pregnant women and the amount of assistance provided so far. Of the 399 pregnant women, only 18.8% (75 women) can be assisted through classes for pregnant women. The next problem is the low reading interest of mothers in KIA books. With the availability of digital educational media, it is hoped that this can be a solution to the problems occurring at the Duren Posyandu. The purpose of this activity is to increase the knowledge of midwife cadres regarding digital education models that can accommodate more pregnant women by using educational materials that are more interesting to pregnant women. The community service activity was carried out on July 31 - August 1, 2025, with 5 village midwives and 13 cadres participating. The activity began with a pre-test, followed by the community service team delivering the material, and finally a post-test. All participants in the activity experienced an increase in knowledge for 20 points, as evidenced by their post-test scores, which were higher than their pre test scores.
Evaluasi Komparatif Algoritma Naïve Bayes, KNN, Logistic Regression, SVM, dan Extra Trees untuk Analisis Sentimen Tokopedia Ciputra, Indramawan; Fahmi, Amiq
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8537

Abstract

The rapid evolution of digital technology has catalyzed a shift in consumer behavior, particularly in online shopping activities facilitated by e-commerce platforms such as Tokopedia. User-generated reviews yield large-scale textual data that can be systematically analyzed to uncover consumer sentiment in a factual and structured manner. This study aims to evaluate and compare the performance of five sentiment classification algorithms Naive Bayes, K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), and Extra Trees Classifier based on user review data from Tokopedia. The analytical workflow begins with web crawling, followed by text preprocessing procedures including tokenization, case folding, and stop-word removal, culminating in sentiment classification using the aforementioned algorithms. Performance evaluation was conducted using four standard metrics accuracy, precision, recall, and F1-score. The results reveal that SVM achieved the highest accuracy at 85%, outperforming KNN and Extra Trees Classifier (84%), Logistic Regression (82%), and Naive Bayes (79%). SVM’s superior performance is attributed to its ability to identify optimal hyperplanes that effectively separate sentiment classes, particularly in high-dimensional feature spaces. These findings offer practical insights for developers of sentiment analysis systems in selecting the most effective algorithm, while reinforcing the strategic application of Natural Language Processing (NLP) techniques within Indonesia’s e-commerce landscape.
An Integrated K-Means and Composite Risk Scoring Framework for Urban Dengue Vulnerability Mapping Alif, Moh. Fachri; Fahmi, Amiq
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.v10i1.15748

Abstract

The rising incidence of dengue hemorrhagic fever (DHF) in Indonesian urban areas highlights the urgent need for analytical frameworks capable of capturing spatial heterogeneity in vulnerability while supporting targeted public health interventions. However, most existing dengue vulnerability studies rely on clustering or indicator-based scoring in isolation, limiting interpretability and reducing their operational relevance for policy-driven decision making. This study explicitly addresses this gap by proposing an integrated spatial clustering and epidemiologically weighted composite risk scoring framework for urban dengue vulnerability mapping. Using Semarang Municipality as a case study, K Means based spatial clustering was combined with composite risk scoring to analyze dengue vulnerability across administrative subdistricts. Seven key indicators consisting of population density, area size, total population, morbidity, mortality, incidence rate, and health facility availability were processed through systematic imputation, normalization, and attribute selection to ensure data consistency and analytical robustness. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, after which K-Means clustering was applied to generate spatially coherent vulnerability groupings. A composite risk scoring mechanism was subsequently employed to classify regions into five operational risk categories: Low-Risk, Moderate-Risk, High-Risk, Very High-Risk, and Emergency-Priority. The results reveal clear structural differentiation in dengue vulnerability patterns, where Emergency-Priority and Very High-Risk clusters are not only characterized by elevated epidemiological indicators but also by constrained health service availability, amplifying outbreak susceptibility. Specifically, 13 subdistricts (7.5%) were identified as Emergency-Priority and 22 subdistricts (12.4%) as Very High-Risk, together accounting for approximately 20% of the study area. Beyond numerical classification, the integration of spatial clustering and composite risk scoring enhances interpretability by linking cluster structure with epidemiological severity and service capacity, thereby improving policy relevance compared to conventional clustering-only approaches. Validation through heatmap visualization, risk category distribution, and cluster ranking confirms the stability and interpretive clarity of the proposed framework. By moving beyond descriptive clustering toward an integrated analytical model, this study contributes a scalable and adaptive decision-support framework for dengue risk mapping. The findings provide actionable insights for policymakers, enabling evidence-based prioritization, optimized resource allocation, and the development of responsive intervention strategies to mitigate dengue burden in complex urban environments.
Penerapan Algoritma K-Means untuk Identifikasi Pola Spasial Insidensi Demam Berdarah Dengue: Studi Kasus Kota Semarang Putra, Wahyu Bagus Wicaksono; Fahmi, Amiq; Erawan, Lalang
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol. 11 No. 3 (2025): Volume 11 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i3.101302

Abstract

Demam Berdarah Dengue (DBD) merupakan ancaman kesehatan masyarakat yang signifikan di wilayah tropis, termasuk Kota Semarang, Jawa Tengah, Indonesia, dengan tingkat heterogenitas spasial insidensi yang bervariasi antar kecamatan. Penelitian ini bertujuan untuk mengidentifikasi pola spasial insidensi DBD melalui pendekatan pembelajaran tanpa pengawasan (unsupervised learning) menggunakan algoritma K-Means clustering. Data epidemiologis yang bersifat komprehensif pada periode 2020–2024, mencakup variabel jumlah populasi, kasus terkonfirmasi, dan fatalitas, diekstraksi dari sistem informasi publik TUNGGAL DARA yang dikelola oleh Dinas Kesehatan Kota Semarang. Metodologi yang diterapkan meliputi pra-pemrosesan data, penentuan jumlah klaster optimal dengan metode Elbow dan Silhouette Score, serta validasi menggunakan statistik nonparametrik dengan uji Kruskal-Wallis. Hasil klasterisasi menunjukkan lima kelompok wilayah dengan karakteristik epidemiologis yang berbeda. Temuan penting mengindikasikan bahwa zona dengan kepadatan populasi rendah secara proporsional menanggung rasio morbiditas dan mortalitas yang lebih tinggi. Pendekatan ini terbukti efisien dalam mereduksi dimensi data epidemiologis menjadi pola spasial yang informatif dan mudah diinterpretasikan. Penelitian ini berkontribusi pada pengembangan pemetaan risiko DBD yang lebih adaptif dan berbasis data serta menyediakan landasan strategis yang kuat bagi intervensi kesehatan masyarakat yang lebih terarah dan tepat sasaran, khususnya dalam konteks studi kasus Kota Semarang.
Identifikasi Faktor Risiko Serangan Jantung di Indonesia Menggunakan Model Prediktif LightGBM Fahmi, Amiq; Muhammad Fais Ramadhani; Ramadhan Rakhmat Sani
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9065

Abstract

Peningkatan prevalensi serangan jantung di Indonesia telah menjadi isu kesehatan publik yang signifikan karena penyakit ini konsisten termasuk penyebab kematian tertinggi secara nasional. Meskipun berbagai studi epidemiologis telah mengidentifikasi faktor risiko klinis maupun perilaku, pendekatan berbasis data untuk prediksi individual masih relatif terbatas, terutama pada konteks populasi Indonesia dengan karakteristik heterogen. Untuk menjawab kesenjangan tersebut, penelitian ini mengembangkan model prediktif serangan jantung menggunakan algoritma Light Gradient Boosting Machine (LightGBM) yang dikenal efisien pada data berukuran besar. Dataset terdiri dari 158.355 observasi dan 28 fitur demografis, gaya hidup, dan indikator medis. Prosedur prapemrosesan mencakup imputasi nilai hilang, pengkodean variabel kategorikal, seleksi fitur menggunakan Principal Component Analysis (PCA), serta penyeimbangan distribusi kelas melalui Synthetic Minority Over-Sampling Technique (SMOTE). Kinerja prediksi dievaluasi menggunakan metrik klasifikasi standar, di mana LightGBM mencapai akurasi 83,39% (train) dan 77,92% (test); presisi 85,67% dan 79,38%; recall 80,19% dan 75,44%; F1-score 82,84% dan 77,36%; serta AUC-ROC 91,84% dan 87,37%. Analisis komponen utama menunjukkan kontribusi varians yang tinggi pada fitur terkait pola konsumsi, penggunaan obat, stres, dan hipertensi. Hasil ini mengindikasikan bahwa LightGBM merupakan pendekatan yang menjanjikan untuk mendukung deteksi risiko serangan jantung secara lebih awal dan berpotensi meningkatkan strategi mitigasi penyakit kardiovaskular di Indonesia.
Evaluasi Komparatif Model Regresi Prediksi Saham BBCA dengan Analitik dan Visualisasi Interaktif Menggunakan Streamlit Fahmi, Amiq; Muhammad Hilmy Munsarif; Agus Winarno
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9119

Abstract

Ketidakstabilan harga saham yang dipengaruhi oleh faktor fundamental dan teknikal menimbulkan kompleksitas dalam proses prediksi serta menuntut model yang akurat dan mampu melakukan generalisasi. Penelitian sebelumnya masih berfokus pada regresi linier konvensional tanpa membandingkannya dengan metode regularisasi maupun menelaah kontribusi rekayasa fitur dalam meningkatkan performa prediktif. Kontribusi penelitian ini adalah mengevaluasi dan membandingkan efektivitas Regresi Linier, Ridge Regression, dan Lasso Regression dalam memprediksi harga penutupan saham PT Bank Central Asia Tbk (BBCA). Kebaruan penelitian ini terletak pada penerapan tahapan preprocessing dan feature engineering yang menghasilkan tujuh variabel turunan, yaitu daily range, open–close change, daily return, lag features, moving average, volatility, dan transformasi logaritmik. Evaluasi model menggunakan Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), dan koefisien determinasi (R²). Hasil utama menunjukkan bahwa Regresi Linier memiliki akurasi tinggi pada data pelatihan namun mengalami overfitting pada data uji, Ridge Regression tidak memberikan peningkatan stabilitas yang berarti, sedangkan Lasso Regression menjadi model paling stabil dengan nilai R² sebesar 0,8247. Temuan ini memberikan manfaat berupa dasar pemilihan metode prediksi yang lebih stabil dan akurat untuk digunakan dalam analisis harga saham dengan volatilitas tinggi.
Spatial Epidemiological Typology of Dengue Risk in Semarang City: A K-Means Clustering Approach Based on Incidence and Fatality Rates Fahmi, Amiq; Anggit Wicaksono, Natanael
Journal of Information Technology and Computer Science Vol. 11 No. 1: April 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.111793

Abstract

Dengue Hemorrhagic Fever (DHF) control strategies in urban Indonesia often rely on uniform interventions that fail to account for the spatial heterogeneity of disease outcomes. While Incidence Rate (IR) is commonly used to map risk, it overlooks the clinical severity represented by the Case Fatality Rate (CFR). This study creates a novel spatial epidemiological typology by integrating both IR and CFR using an unsupervised machine learning approach. analyzing data from 16 sub-districts in Semarang City (2016–2024), we constructed a dual-indicator clustering model. The analysis reveals three distinct risk typologies: (1) High Transmission Zones (High IR), driven by population density; (2) High Mortality Zones (High CFR, Low IR), indicating "silent" risks and potential clinical management gaps; and (3) Controlled Zones. Unlike traditional single-indicator mapping, this proposed typology offers a precise, data-driven framework for decision-makers, enabling the separation of vector control priorities from clinical system strengthening.  
Deteksi Edema Paru Pada Citra Chest X-ray Menggunakan YOLOv5n Dengan Optimasi Hyperparameter Berbasis Grey Wolf Optimizer Nasrudin Affandi Prasetyo; Cinantya Paramita; Amiq Fahmi
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10375

Abstract

Pulmonary edema is a lung disorder characterized by fluid accumulation in the alveolar and interstitial spaces, which disrupts gas exchange and reduces oxygen levels in the blood. Chest X-ray (CXR) imaging is commonly used for pulmonary edema assessment because it is fast and widely available; however, its interpretation still depends heavily on radiologist expertise and may lead to diagnostic variability, particularly in healthcare facilities with limited radiology resources. This study aims to develop an automated pulmonary edema detection system based on deep learning to support more consistent analysis of CXR images. The novelty of this study lies in the integration of Grey Wolf Optimizer (GWO) for hyperparameter optimization of the YOLOv5n model specifically for pulmonary edema detection in Chest X-ray images. The proposed method employs YOLOv5n as a lightweight object detection architecture because of its computational efficiency and suitability for resource-constrained environments. To improve detection performance and training stability, the hyperparameters of YOLOv5n are optimized using GWO. The model is trained and evaluated using annotated CXR images, and its performance is measured using precision, recall, mAP@0.5, and mAP@0.5–0.95. Experimental results show that the YOLOv5n + GWO model achieved a precision of 0.906, recall of 0.936, mAP@0.5 of 0.963, and mAP@0.5–0.95 of 0.737, indicating improved detection performance compared with the baseline YOLOv5n configuration. The proposed framework demonstrates potential as a decision-support tool for assisting medical personnel in early pulmonary edema screening through efficient and consistent analysis of CXR images.
Co-Authors -, Suhariyanto Abdul Rohim, Abdul Abu Salam Agus Winarno Agus Winarno Agus Winarno, Agus Al zami, Farrikh Alif, Moh. Fachri Alzami, Farrikh Andre Septa Wijaya Anggit Wicaksono, Natanael Apriyanti Apriyanti Ardianda Aryo Prakoso Ariel Bagus Nugroho Asih Rohmani Asih Rohmani, Asih Astuti, Yani Parti Budi Harjo Budiono Budiono Candra Irawan Catur Supriyanto Cinantya Paramita Ciputra, Indramawan Diana Purwitasari Edi Faisal Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edy Mulyanto Egia Rosi Subhiyakto, Egia Rosi Eka Prasetyaningrum Erlin Dolphina Etika Kartikadarma Fhaldian, Wahyu Fikri Budiman Fikri Budiman Hadi, Heru Pramono Harun Al Azies Husna, Farida Amila Indra Gamayanto ISWAHYUDI ISWAHYUDI Karis Widyatmoko Kurnia Desita, Raafi Lalang Erawan Laurensius Tokan, Geraldinho Lintang Mekar Tanjung Mauridhi Hery Purnomo Megantara, Rama Aria Moch. Eko Rustiyono Muhammad Fais Ramadhani Muhammad Hilmy Munsarif Muhammad Naufal Muljono, - Mulyanto, Edy Muslih Muslih Muslih Muslih MY Teguh Sulistyono MY. Teguh Sulistyono Nasrudin Affandi Prasetyo Natanael Anggit Wicaksono Noorsidi Aizuddin Bin Mat Noor Nova Rijati Novi Hendriyanto Novi Hendriyanto Prasetya, Rakan Shafy Pujiono Pujiono Pujiono Pujiono Pujiono Purwanto Purwanto Putra, Wahyu Bagus Wicaksono Raden Arief Nugroho Ramadhan Rakhmat Sani Respati Wulandari Ridha Rahmawati Ridho Pambudi Rizky Adrianto Salsabila, Rizka Mars Sidharta, Bayu Adjie Sihombing, Drigo Alexander Sri Winarno Sudibyo, Usman Suharnawi Suharnawi Suryo Adi Nugroho Syifa Sofia Wibowo Tacharri, Chusnuut Tsani, Maulida Aristia Utomo, Danang Wahyu Y. Tyas Catur Pramudi Yumna Huwaida, Imtiyaz Yuventius Tyas Catur Pramudi Zaenal Arifin Zaenal Arifin Zahro, Azzula Cerliana