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All Journal JSAI (Journal Scientific and Applied Informatics) G-Tech : Jurnal Teknologi Terapan
Nurita Evitarina
Universitas Pertiba
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Metode Klasifikasi Kematangan Tandan Buah Segar Kelapa Sawit: Sebuah Tinjauan Sistematis Nurita Evitarina; Kusrini Kusrini
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5050

Abstract

Determining the maturity of palm oil fruit is very important to improve the quality and quantity of palm oil production. This research examines the use of deep learning technology to classify oil palm maturity through a Systematic Literature Review (SLR). The research method used is a Systematic Literature Review (SLR) which involves analysis of 35 journals from Scopus and Google Scholar from 2020 to 2024, with a focus on datasets, algorithms, dataset locations, and methods for measuring model performance. The results show that ANN and CNN are the most widely used algorithms, with usage of 16% and 10% respectively. Accuracy, precision, recall, and F1 score are the most common performance metrics. Future research should focus on improving model generalization and integrating data from multiple sources to improve classification accuracy, the aim of which is to contribute to palm oil maturity classification and help the industry improve production efficiency and quality
Pemodelan dan Simulasi Path Planning Robot Mobil Menggunakan Metode Ant System Pada Lingkungan Terstruktur Rama Saktriawindarta; Suhendri Suhendri; Nurita Evitarina
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9862

Abstract

Path planning is a fundamental problem in mobile robot navigation that requires efficient route optimization while avoiding obstacles. This study implements the Ant System method to solve the mobile robot path planning problem using a modeling and simulation approach in a structured environment. The path planning process utilizes pheromone mechanisms and heuristic information to determine an optimal path from the initial state to the goal state. A total of ten simulation experiments were conducted with variations in the number of intermediate coordinates, iterations, and ants. The results show that the proposed method successfully generated collision-free paths in all experiments, with path lengths ranging from 31.4915 to 32.6788 units. The analysis indicates that the balance between the number of intermediate coordinates, iterations, and ants significantly affects path quality, where well-balanced parameters produce smoother and more stable trajectories. Overall, the Ant System method achieved a 100% success rate, demonstrating its effectiveness and reliability for mobile robot path planning in structured environments.
Pemodelan Pohon Keputusan Menggunakan Algoritma ID3 dalam Pendekatan Data Mining Suhendri Hendri; Rama Saktriawindarta; Nurita Evitarina
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9865

Abstract

The rapid development of information technology has encouraged the use of data mining as a foundation for data-driven decision-making across various sectors, including the karaoke entertainment industry. This study aims to evaluate the performance of the ID3 algorithm in supporting decision support systems through the construction of a decision tree–based classification model. The research method employs the Knowledge Discovery in Databases (KDD) approach, which involves data selection, data transformation, modeling using the ID3 algorithm, and evaluation of decision outcomes. The performance of the method was evaluated based on five key aspects: decision-making capability, classification processing speed, classification result stability, model interpretability, and suitability to user needs. The results indicate that the ID3 algorithm achieved an average success rate of 92%, with the highest performance observed in processing speed and classification stability. These findings demonstrate that the ID3 algorithm is effective, efficient, and highly interpretable, making it suitable for implementation as a classification method in data mining–based decision support systems.
Prediksi Potensi Banjir Menggunakan Machine Learning Dengan Pendekatan XGBoost Dan Logistic Regression Nurita Evitarina; Fitriyanti Fitriyanti; Tri Dewi Yuni Utami
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9867

Abstract

Flooding is one of the most frequent natural disasters in Indonesia, causing significant material losses and casualties. This study aims to develop a flood potential prediction model based on weather data using machine learning approaches, namely XGBoost and Logistic Regression. The dataset consists of 1,513,505 weather records with 1,165 flood events (0.077%). The features include temperature, humidity, wind speed and direction, weather codes, and temporal features generated using a sliding window approach for H-1, H-2, and H-3. Data imbalance was addressed using a combination of stratified undersampling and SMOTE, changing the class ratio from 1:1,298 to 1:3.3. Experimental results show that XGBoost outperforms Logistic Regression, achieving an accuracy of 98.40%, precision of 97.93%, recall of 95.07%, and an ROC-AUC of 99.38%, while Logistic Regression achieved an accuracy of 62.77%. Feature importance analysis indicates that weather codes at H-3 and H-1 are the most influential predictors. With a low false negative rate of 4.9%, the proposed XGBoost model is considered reliable for implementation as a flood early warning system.
Co-Authors Fitriyanti Fitriyanti Kusrini Rama Saktriawindarta Rama Saktriawindarta Suhendri Hendri Suhendri Suhendri Tri Dewi Yuni Utami