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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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Penerapan AHP-TOPSIS pada Sistem Pemeringkatan Jurnal Informatika Berbasis Konsensus Stakeholder Faricha Aulia Azzahra; Syahiduz Zaman; Muhammad Imamudin; Muhammad Ainul Yaqin
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (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.v7i03.1341

Abstract

Selecting a scientific journal for publication is a crucial step for researchers; however, the vast array of available journals often leads to hesitation due to varying quality standards and costs. This study aims to develop an objective decision support system for ranking informatics journals by integrating the Analytic Hierarchy Process and the Technique for Order Preference by Similarity to Ideal Solution methods. This integration is based on a collective consensus regarding criteria weights gathered from 76 expert respondents representing journal users (9 lecturers, 63 students with publication experience, and 4 researchers from the National Research and Innovation Agency/BRIN). The AHP method is employed to determine the criteria weights through consistency testing, while TOPSIS is used to rank 20 alternative national informatics journals indexed in SINTA. The results indicate that the "Aims & Scope" criterion is the top priority with a weight of 0.500, followed by the Accreditation criterion (0.157). The validity testing of the weights yields a Consistency Ratio (CR) of 0.097, indicating that the evaluation is consistent and valid. In the ranking stage, JOIN (Jurnal Online Informatika) secures the first rank with the highest preference value of 0.884. This system successfully provides measurable recommendations for publication venues while minimizing subjectivity in journal selection.
Optimasi Klasifikasi Multikelas Jenis Serangan Jaringan IOT Sunu Ilham Pradika; Hidayat Ramadhani; Imelda Imelda
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (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.v7i03.1342

Abstract

The development of the Internet of Things has encouraged the use of connected devices across various sectors, including healthcare, energy, transportation, and household environments. However, the heterogeneous and dynamic nature of IoT networks, along with their reliance on wireless communication, makes them vulnerable to various cyber threats. This study aims to develop and evaluate an intrusion detection model for multiclass classification in IoT networks using the ToN_IoT dataset. The models used in this study are Random Forest and XGBoost, tested under several scenarios, including baseline models, models with hyperparameter tuning, and the application of SMOTENC to XGBoost to address class imbalance. The research stages include exploratory data analysis, data cleaning, feature selection, data splitting, feature encoding, data balancing, modeling, and evaluation using accuracy, precision, recall, F1-score, training time, and confusion matrix. The results show that all models achieved high performance, with accuracy and F1-score values above 99%. The best performance was obtained by XGBoost with SMOTENC, achieving an accuracy of 99.59% and an F1-score of 99.59%.
Perbandingan Metode Single Exponential Smoothing dan Double Exponential Smoothing untuk Peramalan Omzet Penjualan Café Wildha Anya Prasetya; Putu Indah Ciptayani; Gde Brahupadhya Subiksa
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (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.v7i03.1350

Abstract

The culinary industry of Micro, Small, and Medium Enterprises in Bali faces challenges in operational decision-making, which still tends to rely on intuition due to the suboptimal utilization of historical sales data. This study aims to analyze daily revenue patterns, apply the Single Exponential Smoothing method to forecast future revenue, and develop an interactive dashboard as a data visualization tool. As a comparison to obtain the best accuracy, the SES method is evaluated against the Double Exponential Smoothing method. This research adopts a quantitative approach using a case study of Ruby’s Coffee and Kitchen in Bali, based on daily revenue data from March 2025 to February 2026. The analysis results using the Walk-Forward Validation approach indicate that the SES method with a parameter α = 0.1 achieves the best accuracy with the lowest average Mean Absolute Percentage Error of 25.46%, outperforming the DES method, which produces an average MAPE of 30.73% at the parameter α = 0.1. In addition, usability testing results yield an average score of 4.67 out of 5, suggesting that the dashboard provides an excellent level of ease of use in supporting operational monitoring of the café. The contribution of this study lies in the integration of forecasting methods with a Streamlit-based interactive dashboard, which presents predictive results in a visual and informative manner, thereby assisting management in more efficient operational planning and supporting more accurate data-driven decision-making.
Sistem Rekomendasi Portofolio Berdasarkan Profil Risiko dengan K-Means dan Mean Variance Optimization Gusti Bagus Genta Purusa Arimbawa; Putu Indah Ciptayani; Ni Nyoman Harini Puspita
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (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.v7i03.1369

Abstract

The increasing participation of retail investors in Indonesia has not been accompanied by adequate investment decision-making quality. This issue is reflected in fear of missing out behavior and mismatches between investment choices and risk profiles, particularly among beginner investors. This study develops a personalized stock portfolio recommendation system by integrating K-Means Clustering and Mean-Variance Optimization based on investor risk profiles. The analysis covers 39 liquid LQ45 stocks using four years of historical data. The clustering process identified an optimal K value of 4 with a silhouette score of 0.274, resulting in four stock groups: Low Performers, Value & High Dividend, High ROE, and Aggressive Growth. MVO generated three portfolios with Sharpe ratios between 1.19 and 1.21. The aggressive portfolio achieved an expected return of 24.79% with a volatility of 20.53%. The system was implemented as a web application integrating a BCA-standard risk profiling questionnaire and portfolio weight conversion into exchange-compliant lot units. Unlike a previous Fuzzy C-Means-based approach that used one representative stock from each cluster as MVO input, this study optimizes all 39 stocks and incorporates investor risk profile calibration, producing directly executable portfolio recommendations.
Penerapan Algortima Random Forest Classifier untuk Rekomendasi Produk Skincare Berdasarkan Kondisi Kulit Pengguna Zahra Nurhaliza; Abdul Halim Anshor; Karina Imelda
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (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.v7i03.1383

Abstract

The selection of skincare products that do not match skin characteristics may cause problems such as irritation, acne, and dry skin. However, many users still experience difficulties in identifying their skin condition, resulting in less accurate product selection. This study aims to develop a skin-type classification and skincare product recommendation system based on the Random forest classifier algorithm by utilizing textual product data. The study employed a quantitative approach with an experimental method using secondary data from Kaggle, with ingredients and afterUse as the main attributes. The research stages included preprocessing, rule-based label construction, feature weighting using Term Frequency–Inverse Document Frequency (TF-IDF), an 80:20 data split, model training, evaluation, and implementation of a web-based system using Streamlit. The evaluation results showed that the model achieved an accuracy of 0.8229, weighted precision of 0.8215, weighted recall of 0.8229, and weighted F1-score of 0.8130. Therefore, the Random forest classifier is capable of supporting text-based skin-type classification with good performance and producing a web-based skincare product recommendation system.
Analisis Perbandingan Zero-Shot dan Fine-Tuning pada LLM Untuk Automated Essay Scoring Alexandria Felicia Seanne; Husnul Hadah; Yustika Heti Handal; Britney Levina Sukma; Budi Tjahyono
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (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.v7i03.1452

Abstract

Automated Essay Scoring (AES) using Large Language Models (LLMs) is rapidly evolving, yet there remains a lack of studies comparing Zero-shot prompting and Fine-tuning approaches on open-weight LLM models. This study analyzes the comparison of these two approaches on the Qwen 2.5-1.5B-Instruct model using the ASAP-AES 2.0 dataset. The Zero-shot approach employs Role Prompting and Chain-of-Thought (CoT) techniques, while Fine-tuning uses Parameter-Efficient Fine-tuning (PEFT/LoRA). Evaluation was conducted using the Quadratic Weighted Kappa (QWK) and Root Mean Square Error (RMSE) metrics. The results show that the Zero-shot approach yields a QWK of 0.0069 with an extreme central tendency bias, while Fine-tuning yields a QWK of 0.3247 (fair agreement). These findings confirm that adapting a Fine-tuning approach is one way to produce a sufficiently accurate automatic essay grading system on small-scale open-weight models
Sistem Informasi Alumni (Tracer Study) Berbasis Web dengan Pendekatan User Centered Design Nadia Oktarina; Hery Afriyadi; Try Susanti
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (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.v7i03.1458

Abstract

Tracer studies are an important tool for evaluating the quality of education at a university. However, the Information Systems Study Program at the Faculty of Science and Technology, UIN Sulthan Thaha Saifuddin Jambi, still uses conventional methods to collect, manage, and report alumni data. This is considered ineffective and carries risks such as data loss or duplication, as well as lengthy search and report generation times. Therefore, to address this issue, it is necessary to design and build a web-based alumni tracer study system that is expected to address existing issues. The method used in this study is Agile (Extreme Programming) with a User-Centered Design approach to produce a system that meets user needs. Meanwhile, the tools used are the Laravel and Filament frameworks as panel builders, the PHP and JavaScript programming languages, and the MySQL database system. Based on the results of this study, it is stated that the system can run well according to user needs. Usability testing using Heuristic Evaluation states that the system has a good level of ease of use, while black box testing using the Equivalence Partitioning technique states that all features can run according to their functions, then the results of the User Acceptance Testing received a score of 92%, which is included in the category that is very feasible to be implemented.
Sistem Penilaian Kinerja Tenaga Pendidikan Institut Pariwisata Tedja Indonesia menggunakan Metode Topsis Dila Monika; Opitasari Opitasari; Zuhana Realita Alfy
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (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.v7i03.1474

Abstract

The quality of education is influenced by the presence of qualified human resources who carry out the teaching process in terms of professionalism, work ethics, and interpersonal skills. One important step in improving the quality of educators is to implement an objective and measurable performance appraisal system. The current biased and manual performance appraisal system for educators at the Tedja Indonesia Institute of Tourism creates obstacles, including inaccurate evaluations, inefficiency, and a lack of transparency. This study aims to implement a web-based decision support system using the TOPSIS method to evaluate the competence of educators in a neutral and structured manner. The five characteristics used in the assessment include responsibility, cooperation, honesty, attendance, and communication. The research methods applied include needs analysis, literature review, data analysis, implementation, and testing. The system was developed using PHP 8.2, Laravel 11, MySQL 8, and an interface built with HTML5, CSS3, and JavaScript. Testing results indicate that the system can improve the accuracy and efficiency of the evaluation process and generate performance evaluation reports in PDF format. This system supports more transparent, data-driven decision-making and contributes to fair and professional human resource management in the educational environment.
Implementasi Mediapipe dan Websocket pada Platform Pembelajaran Jarimatika Berbasis Web Muhammad Taufiq Reza; Saruni Dwiasnati; Nur Ghazali Santoso
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (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.v7i03.1497

Abstract

Low student numeracy skills require interactive learning innovations. Jarimatika is effective in aiding counting comprehension, but its conventional application lacks direct feedback and competitive elements. This study aims to design a web-based Jarimatika learning platform integrating Computer Vision technology and real-time network communication. The system development uses the Prototyping model with Black Box testing method. The system implements MediaPipe Hands on the client-side to detect 21 finger gesture landmarks automatically and convert them into Jarimatika values. To facilitate the competition mode, the WebSocket protocol is used to synchronize data between players in real-time, combined with a First-Come-First-Served (FCFS) algorithm to determine the winner. The test results show that the platform successfully detects finger formations accurately and maintains the stability of multiplayer synchronization with a latency of under 500 milliseconds. In conclusion, the integration of artificial intelligence and WebSocket has proven capable of producing an interactive, competitive, and adaptive Jarimatika learning media for users.  
Implementasi Algoritma Apriori untuk Menemukan Hubungan Antar Produk pada Transaksi Penjualan di Aming Coffe Bachtiar Aldy Ramadhani; Samidi Samidi
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (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.v7i03.1509

Abstract

Increasingly fierce competition in the coffee shop industry requires business owners to implement effective, data-driven marketing strategies to boost sales of coffee beverages. Market basket analysis Market basket analysis identifies consumer purchasing patterns by analyzing the relationships between frequently purchased products. The goal is to identify consumer purchasing patterns by analyzing the relationships between products that are frequently purchased together. This study aims to optimize the Apriori algorithm for analyzing consumer purchasing patterns in coffee beverage sales at Aming Coffee. This study uses the Cross-Industry Standard Process for Data Mining framework, which includes the stages of business understanding, data understanding, data preparation, modeling, and evaluation. The data used consists of coffee sales transaction data obtained from the iSeller Point of Sale system for the period from January 1, 2024, to May 31, 2025. The analysis process began with the preprocessing of transaction data, followed by the application of the Apriori algorithm to generate frequent item sets and association rules based on the minimum support and minimum confidence thresholds. The results of this study show that the Apriori algorithm, based on the support, confidence, and lift values obtained, meets the evaluation criteria. Optimizing the minimum support and minimum confidence parameters was found to influence the number and quality of the resulting association rules. It is hoped that the results of this study can serve as an analytical reference to support business decision-making, particularly in understanding consumer purchasing behavior regarding coffee beverage sales, as well as provide an academic contribution to the application of data mining techniques based on the Apriori algorithm.

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