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Alusyanti Primawati, M.Kom
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Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Published by Universitas Indraprasta PGRI
ISSN : -     EISSN : 27158756     DOI : https://doi.org/10.30998/jrami.v7i01
Core Subject :
JRAMI merupakan media publikasi online khusus bagi mahasiswa/i baik didalam Program Studi Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Indraprasta PGRI ataupun luar institusi. Setiap mahasiswa/i yang memiliki hasil riset dari PKM (Program Kreatifitas Mahasiswa) dan atau Tugas Akhir dapat mempublikasinya dalam bentuk artikel ilmiah sehingga kontribusi dari hasil penelitian mahasiswa dapat disebarluaskan dan dimanfaatkan oleh masyarakat luas. JRAMI sejak 2020 diterbitkan sebanyak 4 kali dalam setahun yang dikelola oleh Program Studi Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Indraprasta PGRI. Fokus dan Area Jurnal: Sistem Informasi, Rekayasa Perangkat Lunak, Sistem Berbasis Pengetahuan, Sistem Pakar, E-Commerce, dan Sistem Pengambilan Keputusan.
Arjuna Subject : -
Articles 68 Documents
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Network Automation dengan Metode NDLC Menggunakan Bahasa Pemrograman Python Achmad Fauzi; Desi Desi; Ade Kurnia Solihin
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 01 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i01.789

Abstract

The development of information technology is so rapid, especially on computer networks. Network complexity is a challenge for network administrators in managing many devices, performing maintenance and facing increasingly sophisticated security threats. In this case, network automation is the solusiton. With network automation, the configuration of several network devices can be done simultaneously. The network devices are Cisco routers and switches. The purpose of this research is to create a web-based network design to be carried out with the initial stage of network analysis and problems that arise when building large-svale networks. By utilizing the python programming language, network automation can be implemented. Python has paramiko library that functions as an ssh client. The results of this research built a network automation web for Cisco router and switch network devices, with django as a web developer. So, several devices can be configured simultaneously through web network automation
Sistem Monitoring Suhu Dan Humidity pada Data Center PT Elga Yasa Media Menggunakan IoT Rivaldo Ibrahim; Achmad Birowo; Aan Risdiana
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 01 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i01.790

Abstract

The purpose of this final project is to design a temperature and humidity monitoring system in the data center with in the scope of PT Elga Yasa Media to carry out a manual data acquisition process followed by the development of a monitoring system to facilitate access and accurate monitoring, and be able to provide fast and precise information to make reports that are more structured and in accordance with direct data in the field. The research method is still done manually for the initial data, then the results of the research will produce a system that is installed in the data center of PT Elga Yasa Media, and test the temperature and humidity sensors. The method used for this final project research is the fuzzy method. fuzzy which is one of the software development methods for handling uncertainty and ambiguity in data center environment data. The results achieved are that the temperature and humidity monitoring system at the PT Elga Yasa Media data center is well programmed so that the design for detecting temperature and humidity becomes more effective and efficient, and can be monitored directly without having to make a physical visit to the data center. In building this system, a system design tool, namely Use Case Diagram, for IoT devices and sensors using ESP32 and DHT22, using the C language, using the PHP programming language, MySQL database with XAMPP, and the Laravel framework as a website display of the temperature and humidity monitoring system.
Analisis Prediktif Dropout Mahasiswa Berdasarkan Kinerja Akademik Semester Awal Menggunakan Machine Learning Putu Satya Saputra
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 01 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i01.791

Abstract

Student dropout is a critical issue in higher education. This study aims to develop a predictive model of dropout based on early academic performance using Random Forest and Gradient Boosting algorithms. The dataset, sourced from the UCI Repository, contains 4,424 student records. Key features analyzed include the number of enrolled courses, evaluations, average grades, and enrollment age. Results show that the Gradient Boosting algorithm achieved 70.05% accuracy, while Random Forest reached 70.16%, both performing best in classifying graduates. The model successfully identifies high-risk students, although challenges remain in predicting “enrolled” status. These findings highlight the potential of machine learning for early dropout detection and support more targeted academic interventions.
Sistem Pakar Pendeteksi Kerusakan Sepeda Motor Matik Injeksi Menggunakan Metode Forward Chaining Galih Mustopa; Muhamad Irsan; Muhammad Soleh Ritonga
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 01 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i01.819

Abstract

Currently, there is no expert system application specifically designed to diagnose damage to motorcycle engines, especially automatic injection types. Along with the rapid development of information technology, the implementation of computing technology has become crucial in overcoming various technical problems, including those in vehicle engines. The process of detecting damage to injection motorcycles requires in-depth knowledge and experience that is often not possessed by all mechanics, especially lay users. Therefore, the development of an expert system with the Forward Chaining method is a highly urgent solution. This system is expected to be able to detect damage to automatic injection motorcycles precisely, minimize repair errors (human error), and increase diagnostic accuracy.
Sistem Pakar Untuk Identifikasi Kerusakan Sepeda Motor Menggunakan Metode Forward Chaining pada Bengkel AKC Workshop Erlan Ramadhan; Wanti Rahayu; Sugeng Haryono
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 02 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i02.824

Abstract

Rapid advancements in information technology have driven significant transformations across various sectors, including the automotive industry. One of the challenges faced by motorcycle repair shops is the diagnostic process, which is still performed manually and relies on the technician’s intuition, often leading to inefficiencies and potential errors in identifying issues. This study aims to develop an expert system based on the forward chaining method to facilitate a faster and more accurate initial diagnosis of motorcycle malfunctions. A case study was conducted at AKC Workshop, which faced similar challenges. This expert system was developed using a rule-based approach to mimic an expert’s thought process in diagnosing faults. Based on the test results, when the motorcycle exhibited symptoms G9, G10, G11, and G16, the system successfully identified the fault with a 100% match rate on rule 2 (R2), indicating that the motorcycle had a battery issue (weak battery). These results demonstrate that the developed expert system is capable of providing accurate diagnoses and has the potential to improve the efficiency and accuracy of services at motorcycle repair shops
Perancangan Aplikasi Deteksi Plagiarisme dengan TF-IDF dan Cosine Similarity Shandy Arkan; Heru Sulistiono; Irawan Setiadi
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 02 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i02.842

Abstract

Plagiarism is a serious challenge in the academic world that threatens scientific integrity. Access to commercial plagiarism detection tools like Turnitin is often limited due to high costs, while free tools have word count limitations and data privacy issues. This research aims to develop an offline plagiarism detection application based on natural language processing using the term frequency-inverse document frequency (TF-IDF) and cosine similarity algorithms. This application is designed to operate without an internet connection and without word count limitations and guarantees data security. The development method uses Python 3.13 with the PySide6 framework for the user interface and SQLite as the database. The test results on 1 test document and 9 comparison documents show a detection accuracy with an overall score of 9.38% (SkLearn method). The processing time for each is 8.55 seconds. This application is expected to be an alternative solution for students and educational institutions in independently and safely detecting plagiarism.
Aplikasi Klasifikasi Penyakit Kentang Menggunakan Algoritma Convolution Neural Network (CNN) I Putu Astya Prayudha; Gde Brahupadhya Subiksa; Putu Satya Saputra; I Nyoman Rai Widartha Kesuma Kesuma; Vianne Clarinta Putri Gurning
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 02 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i02.849

Abstract

Potatoes are an important horticultural commodity that is susceptible to various diseases such as black scurf, blackleg, common scab, dry rot, and pink rot. Early disease detection is crucial for reducing losses and increasing productivity. This study aims to develop an image-based potato disease classification model using the Convolutional Neural Network method. The dataset consists of 1,760 images divided into eight classes, with a data split of 70% training data, 20% validation data, and 10% test data. The data processing involves preprocessing and augmentation to improve the model’s generalisation. The convolutional neural network architecture used consists of four convolutional layers combined with batch normalisation, max pooling, dropout, and fully connected layers. The model was trained using the Adam optimiser with a learning rate of 0.0003 and a batch size of 32. Testing results show that the model achieved an accuracy of 86%, with an average precision of 0.87, a recall of 0.86, and an F1-score of 0.86. The evaluation results indicate that the model is capable of recognising most classes well, although errors still occur in those with high visual similarity. This study demonstrates that the CNN method is effective for classifying potato diseases based on digital images and has the potential for further development on larger datasets.  
Software Quality Tourism Industry: Analisis Aplikasi Mobile Nusatrip dengan Metode System Usability Scale (SUS) Gde Brahupadhya Subiksa; I Putu Astya Prayudha; Ni Gusti Ayu Putu Harry Saptarini; I Putu Bagus Arya Pradnyana
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 02 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i02.874

Abstract

Industry using the System Usability Scale. SUS, or the System Usability Scale, was chosen because of its ease in providing a quantitative picture of an application's usability. This study includes the OVO, Spotify, and Mysurabaya applications to explore user perceptions of the interface and functionality of the applications. The research method uses a descriptive quantitative approach with the SUS questionnaire distributed to 42 active user respondents. The SUS score is calculated by summing the scores of each question item and multiplying it by a factor of 2.5. The Cronbach’s alpha test results show excellent internal consistency (0.793). The results indicate that the tested applications received an average SUS score that demonstrates excellent usability, such as NusaTrip with a score of 72.14, above the global average of 68. However, there is variation in user experience that indicates some aspects of the application need improvement. High-scoring items indicate an intuitive interface and well-integrated features, but low-scoring items emphasize the need to reduce the learning curve for new users. The research recommends adding tutorials or a help system to enhance the user experience.
Implementasi Algoritma Apriori Untuk Prediksi Pola Penjualan Produk Fashion pada Toko Innari G-Style Isa Subani; Ni Wayan Parwati Septiani
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 01 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i01.898

Abstract

This research is motivated by the low utilization of transaction data among MSMEs, specifically at Toko Innari G-Style, which still records transactions manually, leading to sales data not being leveraged for strategic analysis. The objective of this research is to develop a web-based sales information system equipped with an analysis feature to identify purchasing patterns using the Apriori algorithm. The research method employs the association rule technique from Data Mining with the Apriori algorithm. Sales transaction data from December 2022 to March 2025 are processed through a pre-processing stage to generate association rules based on support, confidence, and lift values. The result of this research is an information system that successfully and accurately automates transaction recording and reporting. Furthermore, the implementation of the Apriori algorithm successfully identified patterns of frequently co-purchased products, which can be utilized to develop data-driven marketing strategies such as product recommendations and merchandise layout.
Implementasi Metode Forward Chaining untuk Deteksi Dini Serangan Jamur pada Tanaman Padi Berdasarkan Gejala Visual Bagus Sajiwo; Rendi Prasetya
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 02 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i02.937

Abstract

In Indonesia, Oryza sativa is a major food commodity, and disease attacks, particularly those caused by fungal infections, greatly affect its productivity. Fungal infections in rice plants are often difficult to detect early due to the similarity of visual symptoms among different types of diseases and the limited knowledge of farmers. This research aims to implement an expert system that uses the forward chaining method to detect fungal attacks on rice plants early, based on visual symptoms. The forward chaining method is used as an inference mechanism by utilizing IF–THEN rules compiled based on interviews with agricultural experts and literature studies. The expert system was developed in the form of a web-based application and designed using UML modeling. The data used includes visual symptoms, types of fungi, and control methods. System testing is conducted using the black-box testing method to ensure functionality and diagnostic accuracy. The research results indicate that the system is capable of identifying the type of fungus on rice plants well based on the given symptoms and producing appropriate control recommendations. Thus, this expert system is expected to become an effective tool for farmers in detecting and handling fungal attacks more quickly and accurately.

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