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All Journal Jurnal Ilmiah Kursor Scan : Jurnal Teknologi Informasi dan Komunikasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer InComTech: Jurnal Telekomunikasi dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) Applied Technology and Computing Science Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jifosi Nusantara Science and Technology Proceedings Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Informatika Dan Tekonologi Komputer (JITEK) Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika Dan Tekonologi Komputer
Basuki Rahmat
Master Of Information Technology, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Social Sciences Other

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Temperature Monitoring via the Internet of Things Using PID-iTCLab Basuki Rahmat; Minto Waluyo; Tuhu Agung Rachmanto
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3332

Abstract

iTCLab or Internet-Based Temperature Control Lab is a temperature control kit for feedback control applications with an ESP32 Microcontroller, LED, two heaters, and two temperature sensors. The heater power output is adjusted to maintain the desired temperature setpoint. Thermal energy from the heater is transferred by conduction, convection, and radiation to the temperature sensor. Heat is also transferred from the device to the environment. In this paper, it is shown how to program temperature monitoring on the iTCLab Kit via the Internet of Things (IoT) using the Arduino programming language. The controller used is Proportional Integral and Derivative (PID). From the experimental results, the temperature monitoring system works well, and the results of temperature control via a cellphone using the IoT MQTT Panel are shown by the real situation
Optimization of Earthquake B-Value Prediction in Java Using GRU and Particle Swarm Optimization Kesya Nursyahada; Basuki Rahmat; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2521

Abstract

Accurate prediction of earthquake parameters is essential for seismic risk assessment and disaster mitigation, particularly in tectonically active regions such as Java Island, Indonesia. This study presents a novel predictive model for estimating the earthquake b-value a fundamental seismological parameter representing the logarithmic relationship between earthquake frequency and magnitude by integrating a Gated Recurrent Unit (GRU) neural network with Particle Swarm Optimization (PSO). The model is trained using earthquake catalog data from 1962 to 2024, sourced from the Indonesian Meteorological, Climatological, and Geophysical Agency (BMKG). The GRU architecture is selected for its effectiveness in modeling temporal dependencies in seismic time series data. PSO is employed to optimize essential hyperparameters, including the number of GRU units, learning rate, and dropout rate. The optimized model achieves notable improvements in predictive performance: Mean Squared Error (MSE) is reduced from 0.00435 to 0.00030, Root Mean Squared Error (RMSE) from 0.0509 to 0.0173, and Mean Absolute Percentage Error (MAPE) from 3.42% to 1.12%. Training time is also reduced from 57 seconds to 33 seconds, indicating greater computational efficiency. The optimal PSO settings include an inertia weight of 0.8, cognitive and social coefficients of 1.0, 40 particles, and 10 iterations. The primary novelty of this study lies in its targeted application of PSO-optimized GRU architecture for b-value prediction in a seismically complex region. These results demonstrate that evolutionary optimization significantly enhances deep learning performance, providing a robust and efficient framework to support earthquake forecasting and risk mitigation efforts in high-risk zones such as Java Island.
Stacking Ensemble of XGBoost, LightGBM, and CatBoost for Green Economy Index Prediction Andini Fitriyah Salsabilah; Basuki Rahmat; Eva Yulia Puspaningrum
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2530

Abstract

Indonesia faces persistent challenges in achieving sustainable development, particularly in harmonizing economic growth with environmental sustainability. The imbalance among economic, social, and environmental dimensions necessitates a comprehensive and reliable measurement tool to assess progress toward a green economy. The Green Economy Index (GEI), developed by the Ministry of National Development Planning (BAPPENAS), serves this function. However, limited data availability at the provincial level, such as in East Java, hampers accurate evaluation and informed policy formulation. This study aims to develop a machine learning-based predictive model for the GEI using a stacking ensemble approach that combines three powerful algorithms: XGBoost, LightGBM, and CatBoost. The model was built using relevant economic, social, and environmental indicators and evaluated on a holdout dataset to assess its predictive accuracy and generalizability. The results show that the stacking ensemble model achieved superior performance compared to the individual models, recording an RMSE of 0.0298, MAE of 0.0225, and the R² score of 0.9774. In comparison, CatBoost, XGBoost, and LightGBM individually performed with slightly lower accuracy. These findings confirm that the stacking ensemble approach is highly effective for predicting GEI values and offers a practical, data-driven solution for supporting sustainable development strategies at the regional level. The study concludes that such predictive tools can significantly enhance policy planning and monitoring of green economic growth, although further research is recommended to validate the model across other provinces.
Hyperparameter Optimization with Hyperband for Tuberculosis Classification Yovi Ibnu Nasikhin; Basuki Rahmat; Chrystia Aji Putra
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3273

Abstract

Tuberculosis (TB) is an infectious illness that continues to be a major global health challenge due to its high rates of disease and death. Early detection of TB using chest X-ray images often faces challenges related to subjective interpretation by radiologists and limited sensitivity and specificity. This study develops a Convolutional Neural Network (CNN) model to classify chest X-ray images into Normal and Tuberculosis classes, using a total of 2,198 chest X-ray images consisting of 1,173 Normal and 1,025 Tuberculosis samples. Hyperparameter optimization was carried out using the Hyperband algorithm implemented in the Keras Tuner framework to obtain the best parameter combination that produced optimal model performance. The main hyperparameters tuned included the number of dense layers, the number of units per layer, dropout rate, and learning rate. The optimization process yielded the best configuration consisting of two dense layers with 160 and 64 units, a dropout rate of 0.3, and a learning rate of 0.0011. The optimization process increased the model’s accuracy from 0.84 to 0.88 and reduced the validation loss from 0.44 to 0.34, indicating a more stable and effective learning outcome after optimization using Hyperband. The application of Hyperband successfully enhanced learning stability, accelerated convergence, and improved overall model performance. These results indicate that hyperparameter optimization using Hyperband not only enhances CNN-based TB classification accuracy but also strengthens its potential clinical utility by supporting more consistent, rapid, and objective early diagnosis in real-world healthcare settings.
DEVELOPMENT OF AN AIoT-BASED COFFEE BEAN CLASSIFICATION AND SORTING SYSTEM USING A VISION TRANSFORMER Dody Pintarko; Basuki Rahmat; Faisal Muttaqin
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 6 No. 5 (2026): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.22007747

Abstract

Manual coffee bean sorting is highly prone to subjectivity, inconsistency, and low operational efficiency. This study aims to develop an automated classification and sorting system based on the Artificial Intelligence of Things (AIoT). The method integrates a Vision Transformer (ViT) model, TensorFlow Lite, Firebase, and an ESP32 microcontroller within a Mobile–Cloud–Edge Computing architecture. The ViT model was trained on four coffee roast levels to perform real-time inference on Android devices linked to physical sorting actuators. Experimental results showed that the ViT model achieved a 96.87% classification accuracy, while the automated physical sorting mechanism achieved 95.83% accuracy with an average response time of 462 ms. In conclusion, the integration of Vision Transformer and AIoT provides a fast and reliable post-harvest automation solution tailored for smart agricultural applications.
PENERAPAN METODE DEMPSTER SHAFER SISTEM PAKAR UNTUK MENDIAGNOSA KERUSAKAN MESIN MOTOR INJEKSI Taufik Nur Firmansyah; Basuki Rahmat; Henni Endah Wahanan
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 5 (2024): JATI Vol. 8 No. 5
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i5.11156

Abstract

Sepeda motor sebagai kebutuhan primer dalam transportasi terus mengalami perkembangan seperti teknologi injeksi bahan bakar dan transmisi otomatis. Mesin motor injeksi memerlukan pemeriksaan rutin dan perbaikan jika rusak. Dalam hal ini membutuhkan ahli di bidangnya yaitu mekanik. Mekanik perlu mendiagnosa kerusakan sebelum memperbaiki, sehingga membutuhkan alat atau sistem yang membantu. Tujuan penelitian ini untuk mengembangkan sistem pakar diagnosa keruskaan mesin motor injeksi berbasis website menggunakan metode Dempster-Shafer. Metode ini mengelola ketidakpastian dengan cara yang konsisten dan mendekati pemikiran pakar. Hasil penelitian ini adalah sistem pakar berbasis website yang dapat mendiagnosis kerusakan mesin motor injeksi dengan metode yang telah diuji dengan pengujian black box testing, validasi kelayakan, dan uji akurasi. Hasil pengujian black box testing menunjukkan bahwa sistem pakar ini dapat berjalan dengan semestinya sesuai harapan. Hasil pengujian validasi kelayakan sistem pakar menunjukkan bahwa sistem pakar ini layak digunakan, dengan nilai kelayakan 90%. Dan hasil dari uji akurasi ialah 94,38% berdasarkan 16 kasus dari pelanggan bengkel Aneka Motor yang melibatkan 10 pakar. Ketidakakuratan diagnosa sistem sebesar 5,62% dikarenakan adanya perbedaan keyakinan di antara para pakar. Dengan sistem ini, diharapkan proses diagnosis kerusakan menjadi lebih cepat dan akurat, memudahkan mekanik dalam memberikan layanan terbaik kepada pengguna motor injeksi.
PENERAPAN ALGORITMA GRID SEARCH UNTUK MEMPREDIKSI CUSTOMER CHURN PADA BANK MENGGUNAKAN PERBANDINGAN OPTIMASI DECISION TREE DAN RANDOM FOREST Sabrina Laila Sari; Basuki Rahmat; Kartini Kartini
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.16819

Abstract

Industri perbankan saat ini beroperasi dalam ekosistem yang sangat kompetitif, sehingga retensi nasabah menjadi sama pentingnya dengan akuisisi baru. Biaya memperoleh nasabah baru umumnya lebih tinggi dibandingkan mempertahankan nasabah yang ada, sehingga kemampuan mendeteksi potensi customer churn menjadi kebutuhan strategis. Perilaku churn dipengaruhi oleh berbagai faktor seperti demografi, aktivitas rekening, jumlah produk, dan status keaktifan nasabah. Pola kompleks tersebut sulit ditangkap dengan analisis sederhana, sehingga pendekatan machine learning lebih sesuai. Permasalahan utama penelitian ini adalah bagaimana mengidentifikasi nasabah berisiko churn menggunakan data demografi dan perilaku rekening, serta sejauh mana optimasi hyperparameter pada model Decision Tree dan Random Forest dapat meningkatkan performa deteksi, khususnya pada kelas minoritas. Penelitian ini bertujuan untuk membandingkan efektivitas kedua model setelah dilakukan optimasi menggunakan algoritma Grid Search, dengan fokus pada metrik akurasi, recall, f1-score, ROC-AUC, dan PR-AUC. Metode penelitian menggunakan dataset publik berisi 10.000 entri dengan fitur terstruktur, melalui tahapan pra-pemrosesan berupa encoding, normalisasi, analisis korelasi, dan pembagian data pada skenario 70:30 dan 80:20. Hasil pengujian menunjukkan bahwa optimasi hyperparameter meningkatkan kualitas klasifikasi. Decision Tree mencapai akurasi 0.86 namun terbatas dalam mendeteksi nasabah berisiko tinggi, sedangkan Random Forest tampil lebih unggul dengan ROC-AUC 0.838 dan PR-AUC 0.650. Secara keseluruhan, Grid Search terbukti memperbaiki performa model, dan Random Forest direkomendasikan sebagai pilihan utama untuk sistem deteksi customer churn di perbankan
Governance Capability Gap Analysis of SIMLITABMAS: A COBIT 2019-Based Evaluation Methodology and Literature Review Fina Amru Millati; Basuki Rahmat; Faisal Muttaqin
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8460

Abstract

This study aims to evaluate and enhance the Information Technology (IT) governance capability of the SIMLITABMAS system using the COBIT 2019 framework. By integrating a literature study with a formal evaluation methodology, the research focuses its analysis on the Deliver, Service, and Support (DSS) domain, specifically DSS02 (Managed Service Requests and Incidents), DSS03 (Managed Problems), and DSS05 (Managed Security Services). Literature review results indicate that the DSS domain is a critical juncture in university digital services, which frequently encounters obstacles due to manual procedures. Capability measurement results reveal that SIMLITABMAS is currently at Level 3 (Defined) with a fulfillment rate of 75.2%. This finding confirms a one-level gap toward the target of Level 4 (Measured), driven by a high dependency on informal communication channels such as WhatsApp and low digital literacy among users regarding system manuals. As a strategic solution to bridge this gap, this study recommends the implementation of AI Chatbot technology as a 24/7 automated helpdesk. The integration of the COBIT 2019 methodology with this automation solution is proven to transform service governance from an ad-hoc stage toward a more standardized, secure, and quantitatively measurable support system to sustainably support institutional research productivity.
OPTIMASI ALGORITMA XGBOOST UNTUK KLASIFIKASI STATUS GIZI BALITA BB/TB MENGGUNAKAN GRIDSEARCHCV Avi Oktaviani; Basuki Rahmat; Kartini Kartini
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol. 9 No. 2 (2025)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v9i2.5601

Abstract

Masalah gizi tetap menjadi perhatian utama kesehatan masyarakat di banyak negara, termasuk Indonesia. Studi ini merancang dan mengevaluasi sistem klasifikasi otomatis untuk status gizi balita berdasarkan indikator berat badan terhadap tinggi badan menggunakan algoritma XGBoost. Dataset terdiri dari 1.763 anak Indonesia berusia 24–60 bulan, dengan status gizi dikategorikan menjadi enam kelas (gizi baik, risiko gizi lebih, gizi kurang, gizi buruk, gizi lebih, obesitas). Variabel prediktor meliputi jenis kelamin, usia dalam bulan, berat badan, tinggi badan, dan skor Z berat badan terhadap tinggi badan. Praproses meliputi pembersihan data, penghapusan duplikat, pengkodean label, standardisasi, dan pembagian data latih-uji 80:20. Model XGBoost dasar dibandingkan dengan model yang disetel menggunakan GridSearch dengan validasi silang 3-fold dan 10-fold. Kinerja dievaluasi menggunakan akurasi, presisi makro, recall makro, skor F1 makro, dan log loss. Model terbaik, XGBoost dengan GridSearch 10-fold, mencapai akurasi 0,8689, skor F1 0,8149, dan log loss 0,3395, lebih baik daripada log loss dasar sebesar 0,4190. Temuan ini menunjukkan bahwa penyetelan hyperparameter XGBoost dengan GridSearch menghasilkan prediksi probabilistik yang terkalibrasi dengan baik dan merupakan alat pendukung keputusan yang menjanjikan untuk deteksi dini kekurangan gizi dan obesitas pada balita di fasilitas perawatan kesehatan primer.
Stres adalah masalah psikologis umum di kalangan Generasi Z, didorong oleh tekanan akademik, perbandingan sosial, dan paparan digital. Deteksi dini sangat penting untuk mencegah masalah kesehatan mental yang lebih parah seperti gangguan kecemasan, burnout Ananda Asa Firstha Affandi; Basuki Rahmat; Retno Mumpuni
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3375

Abstract

Stress is a common psychological issue among Generation Z, driven by academic pressure, social comparison, and digital exposure. Early detection is essential to prevent more severe mental health problems such as anxiety disorders, burnout, or depression. This study aims to optimize a web-based stress detection system using the Recursive Feature Elimination (RFE) method combined with the Random Forest algorithm. A dataset consisting of 500 psychological assessment records and 12 symptom features (G01 to G12) from A3M Consultant Surabaya was used as the basis for analysis. RFE successfully reduced the number of features to six key indicators, such as G01 (anxiety), G02 (emotional instability), G04 (restlessness), G08 (withdrawal), G09 (confusion), and G12 (suicidal thoughts) while maintaining high model accuracy. The baseline Random Forest using 12 features achieved 0.91 accuracy, while the RFE-optimized model with 6 selected features maintained a comparable accuracy of 0.90. The resulting model achieved an accuracy of approximately 0.90 based on Stratified K-Fold Cross Validation, showing consistent performance across folds. The optimized model was then integrated into a web application called “The Z Space,” which combines data driven predictions from Random Forest with rule- based reasoning using Forward Chaining. This hybrid approach ensures both interpretability and accuracy in determining stress levels. The findings highlight that RFE effectively reduces computational complexity without decreasing model performance, making it suitable for real time web implementation in stress detection systems for Generation Z.
Co-Authors Adhi Prasetya, Guntur ADJIE KURNIAWAN, RAKA Alfi Ramadhaniar Ananda Asa Firstha Affandi Andini Fitriyah Salsabilah Anggraeny, Fetty Anggraini Puspita Sari Avi Oktaviani B. Nugroho Daniella, Cherry Deri Setiawan Devan Cakra Mudra Wijaya Dody Pintarko Edi Dwi Prasetyo Eva Yulia Puspaningrum Faisal Muttaqin Faisal Muttaqin faisal, Faisal Fahri Ferdiansyah Fetty Tri Anggraeny Fina Amru Millati Handono, Stevanus Frangky Harianto Hariyanto Hariyanto Helmy Widyantara Henni Endah Wahanan Henni Endah Wahanani Hutama, Dhany Satya I Nyoman Sujana Ida Retno Moeljani Ifnu Wisma Dwi Prastya Iqbal Imani Khoirul Akbar Kartini Kartini Kartini Kartini Kartini Kesya Nursyahada Mada Lazuardi Nazilly Mandyartha, Eka Prakarsa Minto Waluyo Mohamad Irwan Afandi Muhammad Alfin Jimly Asshiddiqie Nafis Pratama Putra Naufal Mumtaz, Ahmad Nigel, Mortekiano Nugraha, Novemidu Wilis Nugroho, Budi Nurlaili, Afina Lina Prameswari Reksa Agami Putra, Chrystia Aji R MEHINDRA PRASMATIO Raka Adjie Kurniawan Retno Mumpuni Ridho Aji Pangestu Royan Hisyam Rafliansyah Sabrina Laila Sari Singgih Putra Pratama Singgih Putra Pratama Sudiyarto Syaifullah JS, Wahyu Taufik Nur Firmansyah Tuhu Agung Rachmanto Tuhu Agung Rachmanto Wahyu SJ Saputra Widjajani, Bhakti Wisnu Yovi Ibnu Nasikhin Yuniar Purbasari, Intan Yuniar, Intan