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All Journal Infotech: Journal of Technology Information
Ilham Aditya Chandra
Teknik Informatika, STMIK Mercusuar, Kota Bekasi
Computer Science & IT

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SISTEM PAKAR DIAGNOSIS PENYAKIT PADA KUCING MENGGUNAKAN METODE FORWARD CHAINING BERBASIS ANDROID Raihan Fasya; Karno Diantoro; Samroh Samroh; Ahmad Soderi; Ilham Aditya Chandra
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.602

Abstract

This research was conducted at PT Amazon Pet Indo, a company specializing in pet care and supplies, particularly for cats. The issue addressed is the public’s lack of knowledge regarding cat diseases, including types of diseases, symptoms, and treatment methods. Additionally, there is currently no expert system application available to help cat owners perform initial diagnoses on their own. As a solution, an Android-based expert system application for diagnosing cat diseases was developed using the Java programming language with the Forward Chaining inference method. This method works by matching the symptoms selected by the user with a knowledge base of rules embedded in the system. The application is designed to help users recognize symptoms, identify possible diseases, and obtain information on prevention and initial treatment. Based on black-box testing results, the application is capable of providing diagnostic results consistent with the symptoms entered in the consultation menu and runs smoothly on Android devices. This application can help users obtain information quickly and accurately, enabling them to take appropriate preventive measures and provide initial treatment.
ANALISIS DAMPAK GAME ONLINE TERHADAP PERILAKU SOSIAL DAN KOGNITIF PENGUNA STUDI KASUS PLUS GAMING Siti Aminah; Karno Diantoro; Ilham Aditya Chandra
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.548

Abstract

The development of information and communication technology has driven the growth of online gaming into a global phenomenon, especially among the younger generation. The ease of internet access and the availability of supporting devices have increased the intensity of gaming. Online games can provide benefits in terms of strategic thinking, teamwork, and cognitive development, but excessive use can have a negative impact on social interaction, emotional stability, and user time management. The problem in this study is that as the intensity of online gaming increases, it leads to a decrease in social interaction, emotional disturbance, impulsive thinking, and poor time management in users. These impacts not only interfere with the quality of social and emotional life, but also productivity and the balance of daily activities. Therefore, this study utilized a clustering technique with K-Means algorithm using Orange Data Mining application, to group users based on the duration of play as well as social, emotional, and cognitive indicators. This approach helps to objectively identify groups of users who experience both positive and negative impacts. The analysis resulted in two clusters: C1, containing users with moderate playing intensity and positive behavioral tendencies (56.49%, silhouette 0.588), and C2, containing high-intensity users with negative behavioral tendencies (43.51%, silhouette 0.549). This study aims to map the behavior of online game users based on the intensity of playing, so that the general pattern of positive and negative impacts that appear in different groups of users can be known. The findings are expected to be the basis for further studies on the influence of playing intensity on the balance of users' lives.
APLIKASI KLASIFIKASI PENYAKIT RETINA BERBASIS DESKTOP MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) Baharudin Yusup Habibi; Karno Diantoro; Samroh Samroh; Ilham Aditya Chandra; Ahmad Soderi
Infotech: Journal of Technology Information Vol 12, No 1 (2026): JUNI (In Progress)
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v12i1.603

Abstract

Retinal diseases such as choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen are the leading causes of vision impairment and require early detection through optical coherence tomography (OCT) imaging. The diagnostic process, which is performed manually by ophthalmologists, is relatively time-consuming and may lead to delays in treatment. This study aims to develop a Convolutional Neural Network (CNN)-based retinal condition detection application integrated with a desktop application to assist in the automatic analysis of OCT images. The data used comes from the Kermany OCT Dataset, which consists of 30,000 retinal images divided into four categories: CNV, DME, drusen, and normal. The research stages include image preprocessing, such as resizing to 224×224 pixels, normalization, contrast enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE), and data augmentation. The CNN model was developed using Python with the TensorFlow and Keras libraries to extract image features and classify retinal conditions. Test results show that the CNN model achieved an accuracy rate of 99.73% in classifying retinal OCT images. The trained model was then integrated into a Java-based desktop application so it can be used as a diagnostic support system to facilitate faster and more consistent retinal image analysis. The results of the study indicate that the CNN method is effective for classifying retinal diseases and has the potential to support the early detection of retinal diseases based on OCT images.
Co-Authors Ahmad Soderi Baharudin Yusup Habibi Karno Diantoro Raihan Fasya Samroh Samroh Siti Aminah