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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Teknologi dan Manajemen Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding SNATIF Sistem : Jurnal Ilmu-Ilmu Teknik JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) Conference SENATIK STT Adisutjipto Yogyakarta Management and Economics Journal (MEC-J) JTAM (Jurnal Teori dan Aplikasi Matematika) CYCLOTRON Jurnal Abadimas Adi Buana Jiko (Jurnal Informatika dan komputer) Jurnal Teknik Elektro dan Komputer TRIAC Jurnal Riset Informatika JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech Jurnal Sistem informasi dan informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) CIVITAS (JURNAL PEMBELAJARAN DAN ILMU CIVIC) International Journal of Advances in Data and Information Systems Journal of Computer Networks, Architecture and High Performance Computing Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Teknik Informatika (JUTIF) International Journal of Robotics and Control Systems Jurnal Teknologi dan Manajemen SinarFe7 Jurnal Penelitian Journal of Information Systems and Technology Research JAPI: Jurnal Akses Pengabdian Indonesia Internet of Things and Artificial Intelligence Journal JEECS (Journal of Electrical Engineering and Computer Sciences) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi ITIJ Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal ilmiah teknologi informasi Asia
Dwi Arman Prasetya
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering 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 Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Shapley Additive Explanations Interpretation of the XGBoost Model in Predicting Air Quality in Jakarta Iffadah, Adhisa Shilfadianis; Trimono; Dwi Arman Prasetya
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1286.5 KB) | DOI: 10.34288/jri.v7i3.366

Abstract

Air quality degradation has become an increasing global problem since 2008, including in Jakarta. By 2024, air pollution in Jakarta is estimated to cause 8,400 deaths and losses of around 34 billion rupiah. To address air pollution, air quality prediction is needed using historical data of Jakarta Air Quality Index from January 2021 to May 2024. The XGBoost ensemble model was chosen for its ability to handle complex data and prevent overfitting. And Shapley Additive Explanations (SHAP) to understand how the model makes decisions. Results showed the XGBoost model achieved MAPE 4.44%. Analysis with Shapley Additive Explanations (SHAP) identified PM2.5 was significantly affected by max and PM10 features, while O3, CO, SO2, and NO2 remained relevant. An increase in PM10 tends to increase PM2.5 concentrations, suggesting the need to control this parameter to improve air quality. These results are important to provide a better understanding of the dynamics of air quality as well as provide a reference for the government in formulating more effective policies or preventive measures in Jakarta.
Prediction of Rice Harvesting During the Rainy Season in Kabupaten Lamongan Using Stochastic Frontier Analysis Ningrum, Imelda Widya; Prasetya, Dwi Arman; Trimono, Trimono; Kassim, Anuar bin Mohamed
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1393

Abstract

The agricultural sector plays a critical role in ensuring national food security, yet it faces challenges in achieving technical efficiency due to limited land and input resources. This study aims to model and predict the technical efficiency of rice production in Lamongan Regency during the rainy season using a data science-driven Stochastic Frontier Analysis (SFA) approach. The dataset includes key inputs such as land area, labor, fertilizer, and environmental variables. The methodology involved data preprocessing, feature selection based on Pearson correlation and VIF thresholds, and model validation using metrics like R-squared, MAPE, and log-likelihood. The SFA model demonstrated high predictive capability, with R² values exceeding 0.91 in cross-validation and MAPE under 15%. The low gamma value (? = 0.0100) indicates minimal yet consistent inefficiency. The results suggest that integrating SFA with data science techniques provides an effective framework for identifying inefficiencies and can serve as a decision-support system for evidence-based agricultural policy.
Stock Price Prediction and Risk Estimation Using Hybrid CNN-LSTM and VaR-ECF Febriyanti, Alvi Yuana; Prasetya, Dwi Arman; Trimono, Trimono
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4648

Abstract

Stock price prediction is a major challenge in the financial domain due to high volatility and complex movement patterns. Traditional methods such as fundamental and technical analysis often fail to capture the non-linear characteristics and fast-changing market dynamics, highlighting the need for more adaptive approaches. This study proposes a hybrid deep learning model, CNN-LSTM, which combines CNN's local feature extraction capabilities with LSTM’s ability to model long-term temporal dependencies. To incorporate risk management, the model is also integrated with the Value at Risk (VaR) approach using the Cornish-Fisher Expansion (ECF) to estimate potential losses under extreme market conditions. The study utilizes daily historical stock price data of PT Unilever Indonesia Tbk retrieved from Yahoo Finance. Model performance is evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), where the model achieves an MAE of 78.13 and a MAPE of 2.72%, indicating relatively low absolute and relative prediction errors. These results confirm that the CNN-LSTM approach effectively models stock price movements in dynamic market environments, and the integration with VaR-ECF provides a more comprehensive risk estimate. Thus, this approach not only enhances predictive accuracy but also offers valuable decision-support tools for investors in planning investment strategies.
Develop IoT-Based Automatic Water Gate Control Prototype with Fuzzy Logic Approach Ismail, Jefri Abdurrozak; Aditya, Wigananda Firdaus Putra; Ekawati, Anies; Sari, Anggraini Puspita; Prasetya, Dwi Arman
Jurnal Teknologi dan Manajemen Vol 6, No 1 (2025): January
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat ITATS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.jtm.2025.v6i1.6758

Abstract

This research developed a prototype for an automatic water gate control system that integrates Internet of Things (IoT) technology with a Fuzzy Logic approach. The prototype is designed to monitor and regulate water levels in real-time using ultrasonic sensors connected to an IoT network. The water level data is integrated into Amazon Web Services (AWS) for cloud management. Fuzzy Logic was chosen to enhance the system's accuracy and responsiveness to dynamic and unpredictable water levels. The primary goal of this system is to minimize flood risk and ensure adequate water distribution across various sectors by automatically opening the water gates. In initial testing, the prototype successfully transmitted water level data from the sensors to the AWS cloud server and performed fuzzy calculations according to Fuzzy Logic formulas. The prototype demonstrated good results in managing the opening of the water gates based on the water levels detected by the ultrasonic sensors, showing significant potential for water resource management in urban areas through this system.
IMAGE CLASSIFICATION OF VINE LEAF DISEASES USING COMPLEX-VALUED NEURAL NETWORK Putri, Irma Amanda; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7809

Abstract

Leaf diseases are a serious challenge in the agricultural industry affecting crop quality and yield especially in grapevines. Early recognition and classification of grape leaf diseases is crucial to enable farmers to take appropriate preventive measures in maintaining the health of their crops. The research utilized an innovative approach based on Complex-Valued Neural Network (CVNN) to address the problem. Using Complex-Valued Neural Network (CVNN) this research seeks to identify and classify grape leaf diseases through a series of experiments. A total of 100 images divided into 4 classes namely Black Rot, ESCA, Leaf Blight, and Healthy were collected to train the model. The results show that the trained CVNN model successfully achieved a training accuracy of 100% and a testing accuracy of 97%, demonstrating excellent performance in classifying grape leaf diseases. This states that the proposed approach has great potential to be an effective tool in helping growers manage their vineyards more efficiently and effectively. The developed image processing method is expected to be applied in designing a system to perform image classification of diseases on grape leaves.
CLASSIFICATION OF JAVANESE NGLEGENA SCRIPT USING COMPLEXVALUED NEURAL NETWORK Rahmawati, Adinda Aulia; Muhaimin, Amri; Prasetya, Dwi Arman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 1 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7808

Abstract

Javanese script is one of the traditional scripts in Indonesia used by the Javanese people. The Javanese script used in Javanese spelling basically consists of 20 main characters (nglegena), namely from the Ha to Nga script. Javanese script has very high value, the uniqueness of the script is one thing that must be preserved. However, widespread use of Javanese script has declined as technology has developed. In this context, one of the problems that arises is the difficulty in automatically recognizing and classifying the Javanese Nglegena script. Therefore, the use of computational methods to automatically classify the Nglegena Javanese script is very important. This research compares 2 methods for classifying Javanese Nglegena script, namely Complex-Valued Neural Network (CVNN) and Convolutional Neural Network (CNN). This research aims to compare the best accuracy between CVNN and CNN. In this study, the Complex-Valued Neural Network method had a higher average accuracy, namely 96.332% and a loss of 0.1834. Meanwhile, the CNN method has an average accuracy of 93.72% and a loss of 0.4254. Artificial intelligence-based Javanese Nglegena script classification technology can help people to recognize the Javanese Nglegena script, especially in the fields of education and culture.
Optimizing Categorical Boosting Model with Optuna for Anti-Tuberculosis Drugs Classification Yosua Satria Bara Harmoni; Kartika Maulida Hindrayani; Dwi Arman Prasetya
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.ijeeemi.v7i2.92

Abstract

Tuberculosis is one of the leading causes of death globally, with death rate reaching 1.30 million by 2022, an increase of 3.2% compared to the previous year. Indonesia is one of the countries with the highest number of tuberculosis cases in the world. The Directly Observed Treatment Short-course (DOTS) plays a role in improving the effectiveness of tuberculosis therapy by ensuring the availability of appropriate anti-tuberculosis drugs. However, errors in drug selection can lead to therapy failure, relapse, and Multi-Drug Resistant (MDR) cases. To overcome this, classification models based on patient medical record data can be used to improve the accuracy of drug selection. This research focuses on developing classification model to determine the type of drug using Categorical Boosting algorithm optimized with Optuna using Tree-structured Parzen Estimator. The data consisted of numerical variables, such as age, treatment duration, and categorical variables, such as history of diabetes mellitus, HIV status, drug combination. The CatBoost algorithm was chosen due to its ability to handle categorical data. Hyperparameter optimization was performed to obtain the best parameters. The preprocessing stage involved memory reduction, feature normalization, and encoding on 620 data samples, which were then divided into 90% training and 10% test data. Experimental results show CatBoost model produces an initial accuracy of 90%. After applying parameter optimization techniques using Optuna, the accuracy increased to 96%, showing 6% improvement. The model is able to accurately classify drugs combination, which can support the selection of more effective therapies for tuberculosis patients. Thus, the use of SMOTE to address class imbalance combined with Optuna for hyperparameter optimization was shown to improve the accuracy of CatBoost-based classification models. This finding confirms the effectiveness of SMOTE and Optuna methods in improving the accuracy of prediction models for drug type classification, contributing the improvement of tuberculosis treatment strategies.
Multimodal Detection of Covert Online Gambling Advertisements Using Faster R-CNN and Tr-OCR Maldini, Andry Syva; Saputra, Wahyu Syaifullah Jauharis; Prasetya, Dwi Arman
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.v8i1.2769

Abstract

The increasing prevalence of online gambling advertisements on social media has led to the use of covert strategies, such as embedding visual watermarks and employing euphemistic language, to bypass traditional detection methods, rendering manual moderation ineffective. This study proposes an AI-based automated detection system designed to identify both explicit and obfuscated gambling content. The system operates in three stages: (1) Object detection: Faster R-CNN, using a ResNet-50 backbone and Feature Pyramid Network (FPN), detects gambling-related visual elements, such as watermarks and logos; (2) Text extraction: A Transformer-based Optical Character Recognition (TrOCR) model is employed to extract textual content from images and video frames, even in the presence of visual distortions; and (3) Text classification: A BERT-based Natural Language Processing (NLP) model is used to identify gambling-related language within the extracted text. The dataset, manually collected and annotated, was augmented with Roboflow to improve model robustness and generalization. Experimental results show that the Faster R-CNN model achieved an average precision of 98.1%, TrOCR demonstrated a Character Error Rate (CER) of 4.6% and a Word Error Rate (WER) of 29%, while the BERT classifier reached an impressive 99% accuracy with high precision and recall. The system was integrated into a Flask-based web application that allows real-time analysis of both image and video inputs. This system presents strong potential to support automated content moderation and curb the spread of online gambling advertisements on digital platforms, contributing to safer online spaces.
LONGITUDINAL MODELING OF E-COMMERCE CHOICE USING LATENT GROWTH CURVE TO ASSESS INFLUENCING FACTORS AMONG LATE ADOLESCENTS Agustina, Fadlila; Prasetya, Dwi Arman; Damaliana, Aviolla Terza
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/hv8z4172

Abstract

The rapid growth of e-commerce in Indonesia has significantly influenced consumer behavior, particularly among late adolescents aged 18–21 years. This study examines the dynamic factors affecting e-commerce preferences, including price, service quality, and customer loyalty, using Latent Growth Curve Modeling (LGCM). This method was chosen for its ability to analyze variable changes longitudinally, allowing the identification of growth patterns and factors influencing shifts in consumer behavior over time. Data were collected through an online survey involving 400 respondents over three time periods. The study’s findings reveal that price is the most stable variable (intercept 0.5302, slope 0.0811), whereas service quality (intercept 0.8127, slope -0.0285) and loyalty (intercept 0.8508, slope -0.0188) show slight declines. Innovation, functioning as a covariate, significantly affects the intercept of all variables, particularly loyalty, although its impact on growth rates varies. The model demonstrates a good fit, with RMSEA (0.0730), CFI (0.9844), and TLI (0.9402), confirming its validity. Visualizations indicate that loyalty evolves more dynamically than service quality, highlighting the crucial role of innovation in customer engagement. This study emphasizes the need for e-commerce platforms to prioritize innovation and service quality improvements to foster long-term loyalty. These findings provide valuable insights into consumer behavior dynamics and offer strategic recommendations for achieving competitive advantage in the digital marketplace.
IMPLEMENTATION OF KERNEL COMBINATION GAUSSIAN PROCESS REGRESSOR IN LOYALTY PREDICTION (CASE STUDY: ONLINE MOTORCYCLE TAXI) Aziziyah, Luqna; Prasetya, Dwi Arman; Trimono, Trimono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/nm9b4w40

Abstract

In the application-based transportation industry, customer loyalty is a crucial factor affecting service sustainability. This study aims to analyze and predict customer loyalty in online motorcycle taxi services in Surabaya using the Gaussian Process Regressor (GPR) with a kernel combination approach. Data were collected through a survey of 467 students from public universities in Surabaya, considering service quality, price, and innovation factors. The analysis process includes data processing, validation, cleaning, and modeling using Gaussian Process Regression techniques. The results indicate that the kernel combination in GPR effectively captures complex non-linear patterns in survey data, with low Root Mean Squared Error (RMSE) and R² values close to 1. These findings suggest that the proposed approach can provide accurate customer loyalty predictions. This study contributes to developing strategies for online motorcycle taxi service providers to enhance user experience and maintain market share. The findings highlight the importance of applying machine learning models to understand customer behavior and support data-driven business decision-making.
Co-Authors ', Nachrowie ., Humaidi A. A. Ngurah Gunawan Aan Nehru Awanto Achmad Junaidi Aditya, Wigananda Firdaus Putra Agustina, Fadlila Akio Kitagawa Alam, Fajar Indra Nur Ali, Munawar Amrullah, Ahmad Wildan Andre Leto Andrew Arjunanda Yasin Anggraini Puspita Sari Anindha Lazuardi Aries Boedi Setiawan Arifani, Kahpi Baiquni Arifuddin, Rahman Arum Puspita Ayu Atiana Sofia Kaci Awang, Wan Suryani Wan Azizah, Alisa Jihan Aziziyah, Luqna Baidowi Baidowi Baidowi Baidowi Bambang Nurdewanto Barus, Indra Basitha F Hidayatulail Cahyani Kuswardhani, Hajjar Ayu cahyono, wahyu eko Candra Laksana Damai Arbaus, Damai Damaliana, Aviolla Terza Danang - Destiawan Danang Destiawan Desyderius Minggu Dicky Kurniawan Diyasa, I Gede Susrama Mas Dody Pintarko Dwi Agung Ayubi E, Nachrowie Ekawati, Anies Eko Wahyu Prasetyo Elta Sonalitha Sonalitha Erik Roma Hurmuzi Fahrudin, Tresna Maulana Farhans, Muhammad Izzudin Febriyanti, Alvi Yuana Firdaus Firdaus Firza Prima Aditiawan Gatut Yulisusianto Halim, Christina Hari Fitria Windi Hendry Yudha Pratama Hesti Sholikah, Hesti Hidayatulail, Basitha F Hindrayani, Kartika Maulida Hiroshi Suzuki Hurmuzi, Erik Roma Ibrahim, Mohd Zamri Bin Iffadah, Adhisa Shilfadianis Indra Barus Irsyadi, Muhamad Haidir Ismail, Jefri Abdurrozak Januar, Teddy Jariyah Jeki Saputra Junita Junita Kartika Maulida Hindrayani Kassim, Anuar bin Mohamed Kholid, Fajar Kukuh Yudhistiro, Kukuh Kurniawan, Dicky Kusuma, Dwi Febri Chandra Kusuma, Firdaus Miftakh Kuswardana, Dendy Arizki Laksana, Candra Lestari, Amanda Ayu Dewi Lisanthoni, Angela Maldini, Andry Syva Mas Diyasa, I Gede Susrama Maulidiyyah, Nova Auliyatul Mohammad Ansori Mohammad, Bawazir Fadhil Muhaimin, Amri Muhammad Ansori Muhammad Muharrom Al Haromainy Mulyadi Mulyadi Nachrowie Nachrowie Nachrowie, Nachrowie Nambo Hidetaka Ningrum, Imelda Widya Ninik Sisharini Ninis Herawati Norma Windiyanti Novita Anggraini Nur Rachman Nur Rachman Supatmana Muda Nur Rochman Nur Rochman Nurhalizah, Cesaria Deby Prakoso, Akbar Tri Puput Dani Prasetyo Adi Putri, Irma Amanda Rabi, Abd. Rahman Arifuddin Rahmanda Putri, Endin Rahmawati, Adinda Aulia Respati Respati Rosariawari, Firra Rudi Wilson Sagita Rochman Salim, Hotimah Masdan Santika, Surya Saputra, Wahyu Syaifullah Jauharis Sari, Andina Paramita Siswanto Siswanto Siti Nuurlaily Rukmana, Siti Nuurlaily Stanislaus Yoseph Subairi Subairi Sumartono Sumartono Sumartono Suprayogi Suprayogi Suprayogi Suprayogi Surya Nanda Santika, Surya Takahiro Kitajima Takashi Yasuno Tresna Maulana Fahrudin Trimono Trimono, Trimono Wahyu Dirgantara Wahyuni, Dinar H S wangge, ferdinandus Weisrawei, Yosef Yasin, Andrew Arjunanda Yohanes U D Sipul Yosef Weisrawei Yosua Satria Bara Harmoni Yunia Dwie Nurchayanie Yusaq Tomo Ardianto