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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika JTAM (Jurnal Teori dan Aplikasi Matematika) SINTECH (Science and Information Technology) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Infotekmesin Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Technology and Informatics (JoTI) Indonesian Journal of Innovation Studies Jurnal Pengabdian Kepada Masyarakat Abdi Nusa ABDINE Jurnal Pengabdian Masyarakat Bulletin of Social Informatics Theory and Application Jurnal Medika: Medika Jurnal Pengabdian Kepada Masyarakat Bersinergi Inovatif
Arsi, Primandani
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
COMPARISON OF RANDOM FOREST, K-NEAREST NEIGHBOR, DECISION TREE, AND XGBOOST ALGORITHMS FOR DETECTING STUNTING IN TODDLERS Bimawan, Zaynuri Ilham; Astuti, Tri; Arsi, Primandani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2629

Abstract

Stunting is a significant health issue in many developing countries, including Indonesia. Advances in health technology have opened new opportunities to improve the accuracy and efficiency of detecting stunting in young children, with one such advancement being Machine Learning technology. This study compares various Machine Learning algorithms for detecting stunting in children. The methodology includes data collection, data exploration, data preprocessing, feature extraction, model classification, and model evaluation. The results show that Random Forest demonstrates superior performance with the highest accuracy of 0.999132, recall of 0.999132, and a macro-averaged F1-score of 0.998906, making it the most consistent model for predicting child nutritional status. K-Nearest Neighbor also shows very good performance with an accuracy of 0.999050 and an F1-score of 0.998748. Decision Tree has an accuracy of 0.999091 and an F1-score of 0.998705, closely matching the performance of Random Forest and KNN. XGBoost, with an accuracy of 0.991033 and an F1-score of 0.987495, performs lower than the other three models. Therefore, Random Forest is the recommended choice for implementing stunting prediction in children.
EFFECTIVENESS HYPERPARAMETER TUNING ON RANDOM FOREST, LINEAR DISCRIMINANT ANALYSIS, LOGISTIC REGRESSION AND NAÏVE BAYES ALGORITHMS FOR DETECTING DOS NETWORK ATTACKS Saputri, Inka; Arsi, Primandani; Isnaini, Khairunnisak Nur
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.4175

Abstract

Denial of Service (DoS) attacks are a major threat to network security, characterized by overwhelming system resources with illegitimate requests. Such attacks can disrupt critical services and cause substantial financial losses. However, there is still a need for a more efficient model to detect DoS attack with high accuracy. The aim of this research is to determine the impact of hyperparameter tuning on the four algorithms to identify the best algorithm for detecting DoS network attacks. The research method involves data preprocessing, feature selection, encoding, balancing using SMOTE (Synthetic Minority Over-Sampling Techinuque) and evaluation using confusion matrix. This research use the NSL-KDD dataset because it is relevant for DoS attack detection and flexible for testing various classification algorithms and utilizing hyperparameter tuning. This study evaluates the effectiveness hyperparameter tuning on several machine learning alghorithms namely Random Forest, Linear Discriminant Analysis (LDA), Logistic Regression and Naïve Bayes in detecting DoS attacks. Results indicate that Random Forest achieves highest accuracy (99,97%) and robust performance across all metrics, demonstrating superior generalization and precision. LDA, Logistic Regression and Naïve Bayes also performed well but fell short of Random Forest in handling complex patterns in the dataset. The utilization of hyperparameter tuning can improve the accuracy, consistency and efficiency of the algorithm so as to optimize the combination of various parameters in detecting DoS attacks. The findings provide valuable insights into selecting suitable algorithms for future implementations in cybersecurity systems.
A Study Concentration Selection With a C4.5 Algorithm, KNN, and Naive Beyes Busyro, Muhammad; Astuti, Tri; Astrida, Deuis Nur; Arsi, Primandani; Subarkah, Pungkas
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4157

Abstract

The course of concentration is a crucial aspect for students at the university amikom purwokerto.This decision doesn't just affect their academic journey., but also determine their readiness in the face of the working world.Various factors that affect the concentration selection, the challenges that students face, as well as solutions to help them choose concentrations that fit their interests and career goals.There are still many students who have been confused in deciding which courses best fit their interests and career goals..This confusion is often caused by a lack of adequate information and proper guidance. This study attempts to analyze the lecture amikom purwokerto concentration of students in the universities of the use of the method c4.5 algorithm 3, k-neareset naighbors and naïve beyes. Academic student data used as the basis analysis to determine the dominance in the lecture concentration.Of the result of the research uses phon 60,24 % decision is, there are using k-neareset naighbors 75.36 % and use naïve beyes 100,00 % there are, the prediction could be the basis for deciding the lecture the concentration by mainstream student.The result is expected to help the university in recommended it to students study concentration related to the election.
IMPLEMENTATION OF HYPERPARAMETER TUNING IN RANDOM FOREST ALGORITHM FOR LOAN APPROVAL PREDICTION Sandhi Bhakti, Dwi; Prasetyo, Agung; Arsi, Primandani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2032

Abstract

The risk of non-performing loan is a significant issue in the financial industry, including banks and cooperatives. Loan default risks can occur due to various reasons, and one of them is the negligence of staff or subjective decision-making in loan approval. The proposed solution is to enhance an objective and accurate loan approval decision-making system through the application of machine learning technology, aiming to reduce the risk of loan default. The Random Forest algorithm has proven to be the best in predicting loan approval compared to other supervised learning models. Optimization was performed on the Random Forest algorithm through hyperparameter tuning and data balancing using SMOTE. The best accuracy obtained from several experiments was 86.2%. By implementing optimizations on the Random Forest algorithm, it is expected that the model can make loan approval predictions more objectively and accurately, serving as a reference for future loan approval system development.
Co-Authors Adam Prayogo Kuncoro Aditya Permana, Reza Agung Prasetyo Astrida, Deuis Nur Asyari, Muhammad Hasyim Azzahra, Delia Oktaviana Bimawan, Zaynuri Ilham Busyro, Muhammad Dedi Purwanto, Dedi Dhanar Intan Surya Saputra Farizi, Amar Al Firmanda, Reza Arief Hendra Marcos Isnaini, Khairunnisak Nur Junianto, Haris Khofiyah, Salma Ngarifatul Kholifah Dwi Prasetyo Kartika, Nur Kisma, Atmaja Jalu Narendra Kusuma, Bagus Adhi Lestari, Vika Febri Nandang Hermanto Nasar Ghanim, Nadif Nur Ikhsan, Ali Prasetyo Kartika, Nur Kholifah Dwi Prayoga, Iphang Riandini, Dini Rofiqoh, Dayana Salsabiela, Ayuni Sandhi Bhakti, Dwi Saputri, Inka Sari, Rida Purnama Septiana Putri, Refida Subarkah, Pungkas Sugiarti Sugiarti Suhaman, Jali Tarwoto, Tarwoto Tri Astuti V, Jay Waluyo, Retno