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INDONESIA
Jurnal Riset Informatika
Published by KresnaMedia Publisher
ISSN : 26561743     EISSN : 26561735     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Riset Informatika, merupakan Jurnal yang diterbitkan oleh Kresnamedia Publisher. Jurnal Riset Informatika, berawal diperuntukan menampung paper-paper ilmiah yang dibuat oleh peneliti dan dosen-dosen program studi Sistem Informasi dan Teknik Informatika.
Arjuna Subject : -
Articles 432 Documents
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Complex-Valued Neural Network And Fuzzy Inference System For Image Diagnosis Of Rice Leaf Diseases Mutiara Irmadhani; Syaifullah JS, Wahyu; Idhom, Mohammad
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.370

Abstract

Rice serves as a crucial food crop and holds significant importance in Indonesia's agricultural sector. so the health of rice leaves determines the productivity of the crop. Serious problems such as crop failure often occur due to leaf disease attacks caused by pests or unfavorable climatic factors. Controlling these diseases requires proper knowledge so as not to cause negative impacts on the ecosystem due to misdiagnosis. This research develops a Complex-Valued Neural Network (CVNN) and Fuzzy Inference System (FIS) based method to identify the type of disease and determine its severity. CVNN was used to classify leaf images based on detected visual traits, while FIS analyzed the relationship between these traits and disease severity using fuzzy rules constructed from expert data or input. The results show that CVNN provides superior performance compared to CNN, CVNN model with an accuracy of 92%, where all classes produce high and balanced. While the CNN model also provides satisfactory results with an accuracy of 89%, although there is still an imbalance in some classes. The results of the FIS model on the image The severity of the image of rice leaf disease is the most high category in the leaf blast class is the highest of all classes. The combination of CVNN and FIS model proves that this hybrid approach is effective to support diagnosis, so it can help farmers in making early and precise decisions.
DECISION SUPPORT SYSTEM FOR SELECTION OF OUTSTANDING PROSPECTIVE STUDENTS AT SMKS AL WASHLIYAH 2 MERBAU SCHOOL USING PROFILE MATCHING Wardaniah, Sabina; Listia, Hijka; Wulandari, Siti; Yandra Niska, Debi
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.371

Abstract

The selection of outstanding students in the school environment is an important process that requires objective and structured assessment. However, in practice, this process is still often done manually so that it has the potential to cause subjectivity and inaccuracy in decision making. This study aims to build a Decision Support System (DSS) in the selection of prospective outstanding students at SMKS Al-Washliyah 2 Merbau using the Profile Matching method. This method works by comparing the competencies or criteria possessed by students with the ideal profile that has been determined so that the gap (difference) between the two can be identified. The criteria used in the assessment include academic aspects, organizational activity, personality and discipline. This system is implemented in the form of a computer-based application to facilitate the selection process and improve assessment accuracy. The implementation of Decision Support using the Profile Matching method can be concluded that the system built is able to provide an effective solution in the selection process of prospective outstanding students at SMKS Al-Washliyah 2 Merbau.
LONG BEAN LEAF DISEASE IDENTIFICATION SYSTEM BASED ON MOBILE USING CONVOLUTIONAL NEURAL NETWORK (CNN) METHOD Muhamad Fadiah Nurjaman; Purnama Insany, Gina; Sanjaya, Imam
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.373

Abstract

Long beans (Vigna unguiculata subsp. sesquipedalis), have high nutritional value, besides long beans also have a significant role in the economy of farmers in Indonesia. However, the productivity of this plant is often hampered by various diseases that attack the leaves, which can result in a decrease in the quantity and quality of the harvest. This study has succeeded in developing a Convolutional Neural Network (CNN) model with the ResNet-50 architecture to identify six types of diseases in long bean leaves. The dataset used consists of 2,316 images, divided into training data (80%), validation (15%), and testing (5%). The ResNet-50 model, which consists of 50 layers, applies the transfer learning technique by not training the first 35 layers using a specific dataset, but utilizing weights from ImageNet. Training for 100 epochs produces high accuracy, namely 98.3% for training data, 98.4% for validation data, and 98.7% for testing data. Evaluation using Confusion Matrix, Precision, Recal and F1 Score shows very good performance without prediction errors. The final result of this research is a mobile-based software system that can diagnose diseases quickly and accurately, which can help farmers take appropriate action, and support sustainable agriculture in Indonesia.
COMPARATIVE ANALYSIS OF DIMENSIONALITY REDUCTION FOR BREAST CANCER USING MACHINE LEARNING AND DEEP LEARNING Fatimah Asmita Rani; Lufita Marfiana, Duwi
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.375

Abstract

Breast cancer is one of the leading causes of death among women worldwide. Accurate early detection is essential to improve patient survival rates. Therefore, an efficient and optimal detection method is needed. This study presents a comparative analysis between machine learning and deep learning models integrated with various dimensionality reduction techniques to improve the accuracy of breast cancer classification. The dimensionality reduction methods evaluated include Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). This study uses a dataset from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), which includes genetic and clinical data of breast cancer patients. Several classification algorithms are used in the evaluation, including Logistic Regression, Support Vector Machines (SVM), and Convolutional Neural Networks (CNN). Model performance is analyzed based on accuracy, precision, recall, and F1-score metrics. The results show that the LDA technique consistently produces better classification performance compared to other dimensionality reduction methods on various Machine Learning and Deep Learning models. The importance of choosing the right dimensionality reduction method in increasing the effectiveness of learning algorithms and more optimal, especially in the context of complex and high-dimensional medical data. The implications of this study can be used to develop a smarter decision support system in breast cancer diagnosis.
SENTIMENT ANALYSIS OF PLN MOBILE APPLICATION SERVICES USING NAIVE BAYES, SUPPORT VECTOR MACHINE (SVM) AND DECISION TREE METHODS Prabowo, Bagus Adi; Hindasyah, Achmad; Khalid Rivai, Abu
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.378

Abstract

The advancement of information technology has driven public service providers such as PLN to introduce digital innovations, one of which is the PLN Mobile application that enables customers to access various services online. As the number of users increases, numerous reviews have been submitted through the Google Play Store platform, which can be utilized to evaluate service quality. This study aims to conduct sentiment analysis on user reviews of the PLN Mobile application using three classification algorithms: Naïve Bayes, Support Vector Machine (SVM), and Decision Tree. A total of 4,992 review data were collected and processed through text preprocessing stages, including case folding, tokenization, stopword removal, stemming, and vectorization using the TF-IDF method. The data were then split into training and testing sets with a ratio of 80:20 and trained using the three classification algorithms. Model evaluation was conducted using precision, recall, f1-score, and accuracy metrics. The evaluation results indicate that the SVM algorithm delivers the best performance with an accuracy of 94%, followed by Naïve Bayes and Decision Tree, each with an accuracy of 91%. However, all three models demonstrated limited effectiveness in detecting neutral sentiments. Based on these findings, the SVM algorithm is recommended as the most effective model for sentiment classification of PLN Mobile application reviews.
COMPARISON OF THE USE OF TELEGRAM AND BLYNK PLATFORMS IN IOT-BASED GAS LEAK DETECTION Apriliana, Anggie; Ichsan Pradana, Afu; Hartanti, Dwi
Jurnal Riset Informatika Vol. 7 No. 3 (2025): Juni 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i3.381

Abstract

This study discusses the comparison between two Internet of Things (IoT)-based communication platforms, namely Telegram and Blynk, in a gas leak detection this system utilizes the NodeMCU ESP8266 microcontroller to detect hazardous functions to detect gas via the MQ-2 sensor, and measures temperature and humidity using the DHT11 sensor. Additional components such as buzzers, LEDs, relays, and exhaust fans are used as markers and early response when a leak occurs. The development of this system applies This study applies Waterfall method, which consists of planning, needs analysis, design, and implementation stages. This study then focuses on evaluating the effectiveness of both platforms, especially in the aspect. terms of notification speed, ease of integration, and data visualization. Testing is carried out by comparing the response system when detecting a gas leak and sending warning notifications via the Telegram and Blynk applications. This study concluded that Telegram is superior in sending notifications in real time, while Blynk provides advantages in visualizing sensor data and remote device control. In conclusion, the combination of using Telegram and Blynk in one system provides more optimal results than using one platform alone. This system has been proven to increase the effectiveness and efficiency in detecting gas leaks and providing early warnings to users quickly and interactively.
Sales Analysis Using Apriori Algorithm Roja' Putri Cintani; Fitriati, Desti
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i4.351

Abstract

PT JR Pangan Semesta is a company that produces fast food in the form of Donuts and Sweet Bread under the Deroti brand. The sales and promotion methods that have been carried out have weaknesses because the company has difficulty ensuring the right amount of bread production, so there is often excess or lack of stock. In addition, the promotional strategy used has not included the concept of bundling, so the maximum promotional potential has not been fully explored. To overcome these problems, the use of data mining methods is proposed, one of which is the Apriori Association Rule algorithm. Apriori algorithm is used to find consistent sales patterns and find strong product relationships by analyzing sales transaction data. In this study, sales patterns were analyzed at PT JR Pangan Semesta with a minimum support value of 16% and a minimum confidence value of 60%. The analysis results show that there are three products that are often purchased together by consumers, namely Fried Bread, Deroti Donuts, and Eco Donuts. The three products form one valid association rule, so that the rule can be used as a reference for developing efficient production methods for bread and donuts and implementing sales strategies in the form of bundling products to maximize profits.
Classification Of Kredivo Application Reviews Based On User Satisfaction Aspects With The SVM Method Hasanah, Haprilianh; Tukino; Shofa Shofia Hilabi
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i4.390

Abstract

The development of the fintech sector in Indonesia has encouraged the creation of various digital payment applications, one of which is Kredivo which provides instant credit and installments without a credit card. In this study, we analyzed and classified Kredivo application user reviews based on satisfaction attributes using the Support Vector Machine (SVM) method. Review data was collected from the Google Play Store and pre-processed using text preprocessing, InSet dictionary-based sentiment tagging, TF-IDF feature extraction, and training-test data splitting in an 80:20 ratio. Based on the analysis, most Kredivo user reviews were observed to have positive sentiment of 38.70%, negative sentiment of 26.90%, and neutral of 34.40%. The SVM model developed for Kredivo review sentiment labeling works with positive, negative, and neutral. Word cloud visualization recognizes the most important words with positive tones such as "mantap", "baik", "cepat", "mudah", and "transaksi", as well as the most important words with negative tones such as "hapus", "bayar", "bulan", "meminjam", and "tidak". The results of this study can be feedback for Kredivo developers and other fintech platforms to improve services based on user needs and demands, as well as strengthen business strategies according to customer satisfaction levels.
Analysis Of Santri Cleanliness Violation Patterns Using Apriori Algorithm Approach At Salafiyah Syafi'iyah Boarding School Rafi Jawara, Ilham; Zaehol Fatah; Akhlis Munazilin
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i4.393

Abstract

Islamic boarding schools (pondok pesantren) are religious-based educational institutions that emphasize not only Islamic teachings but also the development of character and student discipline, particularly in maintaining environmental cleanliness. However, managing cleanliness often poses a challenge due to low student awareness and the suboptimal implementation of sanitation systems. This study aims to identify behavioral patterns related to student cleanliness through a data mining approach utilizing the Apriori algorithm. This method enables the analysis of students’ habitual behaviors using collected data to uncover hidden patterns related to cleanliness violations. The research was conducted at Salafiyah Syafi'iyah Sukorejo Islamic Boarding School and provides valuable insights for pesantren administrators to design more effective, data-driven cleanliness management strategies. The findings are also expected to enhance student awareness of cleanliness as an essential aspect of Islamic values. Overall, the results serve as a foundation for strategic decision-making to foster a healthy, clean, and supportive environment conducive to optimal learning in Islamic boarding schools.
Information Technology Risk Analysis Using ISO 27005:2022 At Diskominfo Tabanan Regency Astini, Ni Kadek Dheananda; Gusti Agung Ayu Putri; Dwi Putra Githa
Jurnal Riset Informatika Vol. 7 No. 4 (2025): September 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v7i4.394

Abstract

The advancement of information technology (IT) provides significant benefits for organizational operations, including the Department of Communication and Informatics (Diskominfo) of Tabanan Regency. However, IT implementatiocn also brings security risks, such as hacking and cyberattacks, which can threaten the continuity of public services. This study aims to implement risk management based on ISO/IEC 27005:2022 to protect the IT assets owned by Diskominfo Tabanan Regency. The stages carried out include context establishment, risk identification, risk analysis, risk evaluation, and recommendations. In the risk identification stage, 28 IT assets, 57 threats, existing controls for each asset, vulnerabilities of these controls, and potential consequences were identified. In the risk analysis stage, eight respondents were asked to complete a questionnaire to assess the impact of threats and the likelihood of their occurrence, with the average impact and likelihood scores being 3 and 4, respectively. Based on the questionnaire results, the study will proceed with risk level assessment to determine risk levels based on the previous analysis. Subsequently, a risk evaluation will be conducted to provide recommendations for effective mitigation measures. This IT risk analysis study resulted in mitigation recommendations for threats that could potentially impact Diskominfo Tabanan Regency IT assets. The recommendations were developed based on the severity level of each risk after analysis, referring to common practices in both public and private sectors, as well as sources such as research journals, relevant literature, and the ISO/IEC 27005 standard

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