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INDONESIA
INTI Nusa Mandiri
Published by PPPM Nusa Mandiri
ISSN : 02166933     EISSN : 2685807X     DOI : -
Core Subject : Science,
Computer Science & IT
The INTI Nusa Mandiri Journal is intended as a media for scientific studies on the results of research, thought and analysis-critical studies on the issues of Computer Science, Information Systems and Information Technology, both nationally and internationally. The scientific article in question is in the form of theoretical review and empirical studies of related sciences, which can be accounted for and disseminated nationally and internationally.
Arjuna Subject : -
Articles 248 Documents
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OPTIMASI HYPERPARAMETER INDOBERT UNTUK ANALISIS SENTIMEN KENDARAAN LISTRIK DENGAN KOMPARASI ALGORITMA MACHINE LEARNING Elly Indrayuni; Acmad Nurhadi
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8588

Abstract

The presence of electric vehicles has generated diverse public opinions on social media, creating the need for an automated approach to sentiment identification. Transformer-based models such as IndoBERT can capture semantic context more effectively than conventional machine learning methods that rely on feature representations such as TF-IDF, which are less effective in modeling word relationships, particularly in imbalanced datasets. This study aims to analyze public sentiment toward electric vehicles using an IndoBERT model optimized with Grid Search and compare its performance with Naive Bayes and Support Vector Machine (SVM). An experimental method was applied to a dataset of 1,517 Indonesian-language opinions. IndoBERT was fine-tuned using Grid Search by evaluating hyperparameter combinations of epochs (3, 4, and 5), learning rates (2e-5 and 3e-5), and a batch size of 16. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. The best IndoBERT configuration was obtained with 5 epochs, a learning rate of 3e-5, and a batch size of 16, achieving 73% accuracy. Although its accuracy matched that of SVM, IndoBERT produced more balanced results, with a macro F1-score of 0.62 and the highest average AUC (0.780), outperforming SVM (0.758) and Naive Bayes (0.736). The novelty of this study lies in optimizing IndoBERT using Grid Search and comparing it with Naive Bayes and SVM based on ROC-AUC for Indonesian-language electric vehicle sentiment analysis. The findings demonstrate that Grid Search optimization enhances IndoBERT's contextual understanding, resulting in superior overall performance.
PREDIKSI KATEGORI CURAH HUJAN BERBASIS MACHINE LEARNING UNTUK MENDUKUNG KETAHANAN PANGAN I Dewa Gede Loka Maheswara; Kanaya Kaizzi Larasati; Muhammad Nur Rizqi; Muhammad Fany Nurwibowo; Yosafat Donni Haryanto
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8601

Abstract

Rainfall variability significantly influences food security in Central Tapanuli Regency, North Sumatra, a region where agriculture is strongly reliant on climatic patterns. This research evaluates and compares the classification performance of Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Random Forest (RF) for categorizing daily rainfall as supplementary information to strengthen food security. A total of 3,644 daily meteorological records obtained from FL Tobing Meteorological Station spanning 2015 to 2024 were utilized, encompassing seven predictor variables: minimum temperature, maximum temperature, average temperature, mean relative humidity, sunshine duration, peak wind speed, and average wind speed. To mitigate class imbalance, the original six rainfall categories were consolidated into four classes by merging the minority groups. The data were partitioned into training and testing subsets at an 80:20 ratio using stratified sampling, after which the Synthetic Minority Over-sampling Technique (SMOTE) was employed on the training set. Hyperparameter tuning was conducted through Grid Search combined with 5-fold cross-validation, and classification performance was assessed using accuracy, precision, recall, F1-score, and paired t-test analyses. The experimental results indicated that RF delivered superior performance, attaining an accuracy of 51.44% and a weighted F1-score of 0.5036, significantly outperforming both SVM and K-NN (p-value < 0.05). Feature importance analysis revealed that sunshine duration, average temperature, and maximum temperature were the most influential predictors. These outcomes demonstrate that RF holds considerable promise for advancing machine learning-driven rainfall category prediction systems capable of delivering early-stage information for agricultural planting schedules and preparedness against intense rainfall events in Central Tapanuli Regency
TINJAUAN LITERATUR SISTEMATIS KLASIFIKASI CUACA BERBASIS CITRA MENGGUNAKAN METODE DEEP LEARNING Gilang Andhika Buwana; Mohamad Nurkamal Fauzan
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8615

Abstract

Image-based weather recognition has become an important research area due to its applications in intelligent transportation systems, autonomous vehicles, road condition monitoring, and computer vision. Recent advances in deep learning have significantly improved the automatic recognition of diverse weather conditions. However, the variety of image sources, datasets, deep learning architectures, and weather scenarios makes it challenging to obtain a comprehensive understanding of current research trend. Therefore, this study aims to identify the image sources and datasets used in weather recognition research, analyze dominant deep learning approaches, and investigate the weather conditions most frequently addressed in image-based weather recognition and road weather detection. A Systematic Literature Review (SLR) following the PRISMA 2020 guidelines was conducted using the Scopus database. From an initial set of 603 retrieved articles, 73 studies met the predefined inclusion criteria and were selected for further analysis. The results indicate that public datasets such as DAWN, RTTS, Foggy Cityscapes, nuScenes, WeatherDataset-4, and WeatherNet are among the most frequently used data sources. Convolutional Neural Networks (CNNs) remain the dominant approach, although the adoption of Transformer-based models, Vision Transformers, YOLO, Multimodal Fusion, and Multi-Task Learning has increased considerably in recent years. Furthermore, rain, fog, and snow are the most extensively investigated weather conditions due to their significant impact on visibility and perception system performance. The findings provide a comprehensive overview of recent developments in deep learning-based weather recognition and offer valuable insights and directions for future research.
PENGEMBANGAN ARSITEKTUR TERTANAM IOT UNTUK EVALUASI ABRASI PIN-ON-DISC Rizky Muflih; Satrio Adhiyatama Erlangga; Nanang Fajar Untoro; RR Nur Assifaa
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8358

Abstract

Brake pads are crucial elements in vehicle braking systems, requiring wear assessment to ensure safety and braking efficacy. The pin-on-disc methodology is extensively used to investigate the tribological properties of various materials. However, most existing testing equipment still relies on semi-manual data logging, thus lacking the capability to provide real-time information regarding the testing parameters. Furthermore, previous investigations have mostly concentrated on examining the wear and friction coefficients of materials, while the utilization of the Internet of Things (IoT) for monitoring compressive forces remains underexplored. This study attempts to build an IoT-oriented embedded system architecture using an Arduino ATmega 2560 along with a load cell sensor to facilitate real-time monitoring of compressive forces during pin-on-disc testing. The research methodology includes system design, sensor integration, calibration, Arduino programming, and experiments at rotational speeds of 30 rpm and 40 rpm with an applied pressure of 0.2 MPa under dry, water-lubricated, and oil-lubricated conditions. Experimental findings show average compressive loads in dry conditions of 3.5668 kg and 3.1468 kg, in water lubrication conditions of 2.5750 kg and 3.1687 kg, and in oil lubrication conditions of 2.9481 kg and 4.1578 kg. The findings reveal that rotational speed and lubrication conditions affect the contact force characteristics. The originality of this investigation stems from the integration of load cells, Arduino ATmega 2560, and IoT technology for real-time monitoring of compressive forces, while its contribution lies in improving the precision and efficiency of wear testing through continuous digital data acquisition.
SISTEM INFORMASI GEOGRAFIS UNTUK PEMETAAN POHON MANGROVE DI KOTA TERNATE DAN KOTA TIDORE Asrul S. Salasa; Muhammad Ridha Albaar; Hairil Kurniadi Siradjuddin; Salkin Lutfi; Muhammad Fhadli; Ardi Salman; Muhammad Andika Darwis; Ikram Mustafa; M. Mulkan Mufti
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8388

Abstract

The mangrove ecosystems in Ternate City and Tidore City are under pressure due to coastal land conversion, residential development, and other anthropogenic activities. The main problem in mangrove conservation efforts in both regions is the lack of an integrated spatial information system to accurately and easily document the location, type, and condition of mangrove vegetation. This study aims to develop a web-based Geographic Information System (GIS) for mapping and managing mangrove data as a supporting instrument for conservation decision-making. The novelty of this research lies in the integration of field coordinate data, mangrove species identification, visual documentation, and interactive spatial visualization in a single platform that can be used for continuous monitoring of mangrove conditions in Ternate City and Tidore City. The study was conducted at five observation locations: Rua, Gambesi, and Mangga Dua Villages in Ternate City and Rum Balibunga and Mafututu Villages in Tidore City. Data were obtained through field surveys using GPS, mangrove species identification, and field documentation, then processed using PHP, MySQL, JavaScript, and LeafletJS technologies. The results of the study indicate that the system is able to map the distribution of several mangrove species, namely Rhizophora apiculata, Rhizophora mucronata, Bruguiera gymnorrhiza, Bruguiera parviflora, Ceriops tagal, Avicennia marina, and Sonneratia alba accurately and in an integrated manner. Functional testing using the Black Box Testing method confirmed that all system features operated correctly with a 100% success rate.
SISTEM DETEKSI BANJIR DI KABUPATEN BEKASI BERBASIS DATA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE Reza Okta Pratama; Nardi; Anton Widodo; Marzuki Sinambela
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8642

Abstract

Floods are the most common hydrometeorological disasters in Indonesia, and Bekasi Regency is among the areas with a high level of vulnerability. This study aims to develop a Twitter-based flood detection system using Support Vector Machines (SVM), with Logistic Regression (LR) and Random Forest (RF) as comparison models. A total of 4,436 tweets from the 2020–2024 period were collected using Tweet Harvest, manually labeled, and then processed through preprocessing, TF-IDF feature extraction, and data splitting using group-based random splitting (80:20). Evaluation was conducted using 10-fold GroupKFold cross-validation with recall as the primary metric, followed by hyperparameter tuning using GridSearchCV. The evaluation results showed that SVM performed best compared to LR and RF. The tuned SVM model achieved a test recall of 0.8673, exceeding the minimum threshold of 0.80. The model was then integrated into a web-based monitoring dashboard that displays interactive maps, statistics, and temporal trends. Black-box testing across nine scenarios achieved a 100% success rate, while a user experience evaluation using the UEQ-S on 18 respondents yielded an overall score of 1.97, categorized as “Very Good.” The research results indicate that the developed system is capable of effectively supporting social media-based flood monitoring.
ANALISIS PROBALITAS NAÏVE BAYES BERBASIS LAPLACE SMOOTHING KEBUTUHAN RESTOCK FARMASI Arga Fauzi Indratna; Adi Fajaryanto Cobantoro; Ismail Abdurrozzaq Zulkarnain
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8663

Abstract

Drug inventory management plays a critical role in supporting hospital health services. Amal Sehat Wonogiri Hospital faces challenges in managing drug stocks due to manual recording processes prone to errors, data update delays, and inaccurate inventory information, creating risks of stock-out and overstock conditions. This study implements the Naive Bayes algorithm with Laplace Smoothing optimization to classify drug stock conditions into three categories: Safe (Aman), Low Stock (Menipis), and Needs Restocking (Restok). To address class imbalance in the dataset, this study applies SMOTE (Synthetic Minority Over-sampling Technique) and provides a comparative analysis against the Decision Tree C4.5 algorithm. The system was developed using the Rapid Application Development (RAD) methodology with the Laravel framework and MySQL database, utilizing 38,531 historical pharmacy transaction records from January to October 2025. Following a stratified 80:20 train-test split, model evaluation on 7,707 test records yielded an accuracy of 84.04%, with weighted-average precision of 87% and recall of 84%. Five-fold cross-validation confirmed model stability with a mean accuracy of 83.16%. Comparative experiments demonstrated that although Decision Tree achieves higher overall accuracy (87.43%), Naive Bayes with Laplace Smoothing significantly outperforms Decision Tree in detecting the critical Low Stock class (F1-score: 54.73% vs 25.23%; recall: 70.69% vs 16.09%), proving its superiority in minimizing the risk of undetected stock shortages.
IMPLEMENTASI METODE USER-CENTERED DESIGN DALAM PENGEMBANGAN APLIKASI APP STORE BAGI UMKM Nurmalasari; Siti Masturoh; Widi Astuti
INTI Nusa Mandiri Vol. 21 No. 1 (2026): INTI Periode Agustus 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v21i1.8747

Abstract

Small, Medium, and Micro Enterprises (SMMEs) play a crucial role in driving economic growth. Even so, the adoption of digital technology among SMMEs is still quite low, especially in communities with limited digital literacy. This study aims to implement the User-Centered Design (UCD) method in developing an SMME App Store application for the Upgraded SMME Community in Bekasi City. The initial interview results showed that the community has more than 50 MSME members, but only about 20% have used a digital platform to support their business activities. To tackle this issue, a mobile app was developed to help manage business profiles, promote products, categorize products, interact with customers, and handle orders through WhatsApp integration. This research applied the User-Centered Design (UCD) methodology based on ISO 9241-210 standards, which consists of five stages: plan the human-centered design process, specify the context of use, specify user and organizational requirements, produce design solutions, and evaluate design user requirements. Data was collected through observation, interviews, and literature studies as a basis for designing the application. Based on functional testing results using Black Box Testing, usability evaluation using SUS with a score of 81.25, and limited implementation with 30 members of the UMKM Naik Kelas Community in Bekasi City, the developed application has met functional needs and has a good level of ease of use, thus supporting the promotion and marketing of UMKM products.

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