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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Masyarakat Informatika JURNAL SISTEM INFORMASI BISNIS Bulletin of Electrical Engineering and Informatics Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) InComTech: Jurnal Telekomunikasi dan Komputer Journal of Information Systems and Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Aisyah : Jurnal Ilmu Kesehatan Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Biomedical Science and Bioengineering Jurnal Sistem Informasi Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Informatika
Adi Wibowo
Departement Of Computer Science, Informatics, Universitas Diponegoro
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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Klasifikasi Citra Sentinel melalui Google Earth Engine dengan menggunakan algoritma Machine Learning XGBoost Gregorius Anung Hanindito; Adi Wibowo; Budi Warsito
InComTech : Jurnal Telekomunikasi dan Komputer Vol. 16 No. 1 (2026)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v16i1.31354

Abstract

Remote sensing technology and Geographic Information Systems (GIS) have rapidly evolved to provide extensive data and information on land cover. This study aims to monitor land cover in Tanjung Keluang Nature Tourism Park (TWA) and its surroundings using Sentinel satellite imagery on the Google Earth Engine (GEE) platform, employing the XGBoost machine learning algorithm. The methods involved acquiring Sentinel satellite imagery, pre-processing for geometric correction, developing training and testing datasets, as well as performing classification and accuracy evaluation. The results indicate that the XGBoost algorithm can classify land cover into several categories with an accuracy of up to 98%. The classified land cover includes water bodies (23,346 Ha), open land (9,680.54 Ha), sand mining areas (931.15 Ha), and vegetation (16,596.84 Ha). This study contributes positively to the management of conservation areas, particularly in supporting decision-making for TWA Tanjung Keluang in the future.
Security Risk Management Assessment in Information Technology Services using Information Technology Infrastructure Library (ITIL) V4 Andys Sandra Kurniawan; Aris Puji Widodo; Adi Wibowo
Jurnal Sistem Informasi Bisnis Vol 15, No 4 (2025): Volume 15 Number 4 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp469-472

Abstract

Information Technology (IT) is currently implemented in various fields of life, including in higher education. Some common IT-based service problems such as server down, slow systems, poor integration, and data security are important concerns for education managers. IT Service Management or Information Technology Service Management (ITSM) is a strategic approach to designing, providing, managing, and improving the way IT is used in an organization. ITIL is one of the most popular ITSM frameworks and includes a framework for evaluation and assessment. This study proposes the use of ITIL V4 to assess the level of maturity of security risk management in the higher education sector, which has not been widely explored. This study aims to measure and analyze the level of capability and assess the maturity of IT services, especially in risk management practices and information security management and analyze the level of gap between actual conditions that occur and expected standards. The results of the study indicate that the assessment of the level of maturity of higher education in managing IT service security risks, especially in both management practices, is at level 3 (Defined). These results indicate that universities have begun to realize the importance of IT security risk management, where practices are well defined, processes and activities are documented and standardized. To achieve continuous improvement according to the ITIL V4 standard, it is necessary to increase the capacity of the technology used, consistency in evaluation, and build an organizational culture that supports continuous risk management.
Separable Convolutional Hierarchical Decomposition for Lightweight Residential Load Forecasting in Smart Grids Satriawan Rasyid Purnama; Henri Tantyoko; Adi Wibowo; Yesaya Rudolf Susanto Widyanto
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2869

Abstract

Residential load forecasting is essential for maintaining grid stability and energy management in smart grids. However, achieving accurate real-time forecasting under resource constraints remains challenging because Transformer and LSTM models can be computationally demanding, while lightweight linear models such as DLinear have limited modeling flexibility. This study investigates whether a hierarchical separable convolutional framework can provide accurate and efficient residential load forecasting. To address this, SeparableCLF, a lightweight hierarchical decomposition model using depthwise separable convolution, is proposed and evaluated on hourly OpenEI residential load data from 20 U.S. states (2012) at forecast horizons of 6, 12, 24, 48, and 96 h. Relative to DLinear, SeparableCLF reduced MAPE by 0.93, 1.54, and 0.79 percentage points at 24, 48, and 96 h, respectively while requiring substantially fewer parameters than Transformer and LSTM models.SeparableCLF achieved the lowest MAPE at 12, 24, and 48 h. DLinear achieved the lowest errors at 6 h, whereas LSTM achieved the lowest MAPE at 96 h; at 96 h, SeparableCLF retained the lowest MAE, MSE, and RMSE among the compared models, indicating suitability for real-time forecasting on smart meters and edge-based smart grid devices.
Klasifikasi Penentuan skema Uji Sertifikasi di LSP UDINUS bagi mahasiswa Progrdi Sistem Informasi UDINUS dengan Algoritma Decision Tree (C4.5) Agus Winarno; Budi Warsito; Adi Wibowo; Junta Zeniarja
JOINS (Journal of Information System) Vol 7 No 2 (2022): Edisi November 2022
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v7i2.6429

Abstract

AbstrakPenerapan Algoritma Decision Tree (C4.5) dan Eksperimen proses klasifikasi dilakukan menggunakan data nilai mahasiswa program studi S1 Universitas Dian Nuswantoro dengan 120.232 dataset menggunakan metode klasifikasi dengan Algoritma Decision Tree menghasilkan 99,99 % dan menghasilkan 7 klasifikasi  dengan rekomendasi ntuk pelatihan dan uji sertifikasi di Lembaga sertifikasi profesi UDINUS pada  5 klasifikasi yaitu klasifikasi  pred C sejumlah 2.355 data, pred BC sejumlah 4.633, pred B sejumlah 38.420 , pred AB sejumlah  33.414  dan pred A  sejumlah 37.230 dan 2  klasifikasi  yang tidak direkomendasikan yaitu klasifikasi pred D 1.440 dan pred E sejumlah 2.784 yang memberikan penyesuaian kurikulum pendidikan di Universitas Dian Nuswantoro dengan lembaga Sertifikasi dan Kebutuhan pekerjan. Kata Kunci: Sertifikasi, klasifikasi, mahasiswa, Tree Algorithm, rekomendasi
Multitask deep learning for sentiment analysis with sarcasm detection in bilingual code-mixed social media content Mohd Suhairi Md Suhaimin; Adi Wibowo; Ervin Gubin Moung; Patricia Anthony; Mohd Hanafi Ahmad Hijazi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10935

Abstract

Sentiment analysis in social media often hindered by sarcasm, which can reverse text meaning, and bilingual code-mixing, which adds complexity in non-English primary context. Existing approaches extract separate features for each language and translate them into a single language, resulting in the loss of contextual meaning and omission of crucial features. This paper proposes a multitask learning model for sentiment analysis with sarcasm detection tailored to bilingual code-mixed social media content. A hybrid feature engineering technique is integrated into a multitask deep learning architecture designed to capture the nuances of sentiment and sarcasm while addressing the complexities of processing bilingual code-mixed content. The hybrid technique combines domain-knowledge-based natural language processing (NLP) with a deep learning-based embedding approach. It includes rule-based preprocessing, normalization, spellchecking, feature extraction and selection, and feature representation. The engineered features are integrated into a multitask deep learning network using bidirectional long short-term memory (Bi-LSTM) combined with gated recurrent units (GRU). Using a public dataset that contains bilingual code-mixed social media content related to public security, our proposed model achieved a higher F1score compared to two baseline models that employ single task and multitask approaches.
Improving Low Birth Weight Prediction in Indonesia Through Hybrid Resampling and XGBoost Class Weighting with SHAP Interpretation Bramono Triprastowo; Adi Wibowo; Helmie Arif Wibawa
Jurnal Sistem Informasi Bisnis Vol 16, No 2 (2026): Volume 16 Number 2 Year 2026 (In Press)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss4pp%p

Abstract

Newborns are classified as having low birth weight (LBW) if they weigh less than 2,500 grams (5.5 pounds), which poses risks for stunted growth, developmental delays, and long-term health issues such as cardiovascular disease. This study develops an XGBoost based machine learning model for low birth weight classification using the 2017 Indonesia Demographic and Health Survey (IDHS). To address severe class imbalance, various resampling techniques including undersampling, oversampling, hybrid methods, and algorithm level class reweighting are applied, along with a modified random oversampling method (M-ROS). SHAP and XGBoost’s built in feature importance are then used to identify key predictors of low birth weight in the Indonesian context. A total of 16,340 records with 42 preprocessed features were used in this study. The dataset was split into 70 percent for training and 30 percent for testing, and stratified k-fold cross-validation with random search was employed to identify the optimal hyperparameters. Among the resampling strategies evaluated, random oversampling, M ROS, ENN with class reweighting, and class reweighting without any resampling were able to improve recall. Of these approaches, class reweighting without resampling achieved the highest recall at 36.63 percent, while ENN combined with class reweighting produced the highest accuracy at 84.72 percent. Key predictors influencing the classification of low birth weight included the number of antenatal care visits, multiple pregnancy, and place of delivery. The findings demonstrate that machine learning can be effectively utilized to predict low birth weight and to identify influential contributing factors.
Toward AI-Based Child Emotion Early Warning Systems in Child-Oriented Digital Environments: A Socio-Technical Systematic Review Ratih Titi Komala Sari; Widowati; Adi Wibowo
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1811

Abstract

AI-based emotion recognition is gaining increasing attention in child-oriented digital environments, including digital play, AI toys, online learning, therapeutic platforms, and interactive child-computer systems. However, the translation of recognized emotions into responsible caregiver-facing early warnings remains insufficiently explored. This systematic literature review synthesizes technical and socio-technical perspectives on child emotion recognition, focusing on sensing modalities, AI approaches, datasets, alert interpretation, trust, privacy, ethics, governance, and caregiver acceptance. Following PRISMA 2020 guidelines, 400 records from Scopus and IEEE Xplore were screened, resulting in 32 studies included in the core synthesis. The findings reveal that facial-expression analysis and convolutional neural networks dominate current research, while child-specific datasets, multimodal learning, real-world validation, uncertainty communication, privacy-by-design, and caregiver-centered evaluation remain limited. This review proposes a socio-technical framework linking AI emotion inference with explainable alert translation, privacy-aware governance, and caregiver decision support. The proposed emotion-to-alert mechanism remains conceptual and requires empirical validation before practical deployment.
Korelasi CO2 Terhadap Suhu dan kelembapan Dengan Multivariate Linear Regression Whisnumurti Adhiwibowo; Budi Warsito; Adi Wibowo
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.5230

Abstract

Sekarang ini keadaan udara di daerah perkotaan  sudah sangat tercemar oleh polusi. Semakin banyak gas CO2 yang mulai menyebar ke udara dapat menyebabkan meningkatnya suhu udara.. Hal ini dikarenakan bahwa CO2 dapat meningkatkan suhu di permukaan bumi. CO2 mempunyai peran yang dapat menyebabkan pemanasan global karena gas CO2 mempunyai  di udara bebas dan dapat menyerap panas Matahari sehinggs suhu Bumi meningkat dampak pencemaran udara seperti asap kendaraan, asap rokok, asap dari pembakaran pabrik, dan kontribusi terbesar dalam pemanasan global mempunyai pengaruh sebesar 50% dan mempunyai lama hidup 50 200 tahun di atmosfer. Peningkatan suhu udara dan konsentrasi CO2 merupakan masalah yang sering terjadi pada daerah perkotaan dimana salah satunya adalah meningkatnya jumlah kendaraan bermotor sehingga konsentrasi CO2 juga ikut meningkat. Dengan melihat korelasi CO2 Terhadap Suhu dan kelembapan Dengan mengunakan Multivariate Linear Regression, kita dapat melihat bagaiman korelasi antara suhu serta kelembapan. Regresi linier multivariat merupakan model regresi linier dengan lebih dari satu variabel respon Y berkorelasi dan satu atau lebih variabel prediktor X. Hasil penelitian menunjukkan bahwa  Dalam penelitian tersebut dikatakan bahwa terdapat korelasi antara CO2 suhu serta cahaya.  
Enhancing Transparency in Income Prediction: A SHAP-based Explainable AI Approach with Random Forest Marsani Asfi; Budi Warsito; Adi Wibowo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7461

Abstract

Explainable Artificial Intelligence (XAI) has gained increasing importance as modern machine learning models operate as opaque black-box systems, limiting transparency and trust in automated decision-making. While most XAI frameworks merely focus on static post-hoc visualizations, this study goes beyond passive diagnostics by introducing an algorithmic recourse framework for income prediction. By integrating SHAP (SHapley Additive exPlanations) with an optimized Random Forest classifier trained on the UCI Adult Income dataset, we partition socio-economic attributes into actionable features (e.g., weekly working hours, education level) and immutable demographic variables (e.g., age, sex). The dataset was preprocessed using missing-value handling, categorical encoding, class balancing via SMOTE, and feature scaling. Hyperparameter tuning optimization determined that the configuration of 150 trees, a maximum depth of 15, and a minimum split size of 15 yielded peak training accuracy rates of 85.49%, 86.13%, and 86.05% respectively. The Random Forest model achieved strong predictive performance, yielding an accuracy of 84.68%, precision of 85.15%, recall of 84.68%, F1-score of 84.88%, and a high ROC-AUC of 90.95%. Global SHAP analysis identified relationship, marital status, and education as the primary drivers of income classification. Crucially, local instance-level counterfactual optimization demonstrates that strategically adjusting actionable features can successfully neutralize negative SHAP momentum and flip unfavorable low-income predictions (≤ 50K) into positive outcomes (>50K) while holding immutable traits perfectly constant. This integration demonstrates that combining Random Forest with an algorithmic recourse pipeline transforms AI from a descriptive transparency tool into an active, prescriptive mechanism that empowers data-driven decision-making and human solutions in socio-economic applications.
Co-Authors Abraham Timotius Asmoro Putro Adian Fatchur Rochim Agus Winarno Alfania Sarah Handayani Andys Sandra Kurniawan Anief Fauzan Rozi Aris Puji Widodo Asep Insani Aziz Jihadian Barid Bayu Surarso Beta Noranita Beta Noranita Beta Noranita Boko Nurdiyanto S. Bramono Triprastowo Budi Warsito Budi Warsito Budi Warsito Budi Warsito Budi Warsito Budi Warsito Cahyo Adhi Hartanto Dandi Indra Wijaya Dedy Kurniadi Djalal Er Riyanto Djalal Er Riyanto Eka Rahmawati Eko Adi Sarwoko Emigawaty Ervin Gubin Moung Ferda Ernawan Fra Siskus Dian Arianto Gregorius Anung Hanindito Guruh Aryotejo H Hadiyanto Helmie Arif Wibawa Henri Tantyoko Iffa Zainan Nisa Indra Jaya Indra Waspada Junta Zeniarja Kabul Kurniawan Kusworo Adi Marsani Asfi Mohd Hanafi Ahmad Hijazi Mohd Suhairi Md Suhaimin Muh. Abdur Rohman Muh. Abdur Rohman Muh. Abdur Rohman Noel Segura Meraz Nurdin Bahtiar Patricia Anthony Prajanto Wahyu Adi Rahmat Gernowo Rahmat Jayanto Ratih Titi Komala Sari Retno Kusumaningrum Rini Nuraini Riza Arifudin Rizka Putri Nawangsari Satriawan Rasyid Purnama Soni Adiyono Sukmawati Nur Endah Sutikno Sutikno Wahyul Amien Syafei Whisnumurti Adhiwibowo Widowati Wirawan, Panji Wisnu Yesaya Rudolf Susanto Widyanto