cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Alusyanti Primawati, M.Kom
Contact Email
alus.unindra23@gmail.com
Phone
+6281511577299
Journal Mail Official
jramiinformatikaunindra@gmail.com
Editorial Address
Kampus B Universitas Indraprasta PGRI, Jl. Raya Tengah No.80, RT.1/RW.3, Gedong, Kec. Ps. Rebo, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta 13760
Location
Kota adm. jakarta selatan,
Dki jakarta
INDONESIA
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 92 Documents
 6   7   8   9   10 
Sistem Seleksi Penerimaan Karyawan Baru di PT Ilmukomputercom Braindevs Sistema Menggunakan Metode Profile Matching Badr Khaerudin; Rayung Wulan
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 04 (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.v7i04.2033

Abstract

The selection process for new employee recruitment at PT IlmuKomputerCom Braindevs Sistema is still conducted by reviewing application documents and paper curriculum vitae (CVs) one by one by HR personnel and relying on recommendations from partner schools. The number of applicants increased almost threefold, from 36 people in 2021 to 104 people in 2025, making the mechanism time-consuming and vulnerable to subjective assessment. This research aims to design and build a web-based new employee recruitment selection system application using the Profile Matching method. The system development was carried out using the waterfall model SDLC approach, with data collection through literature study, observation, and interviews with the HRD. The profile matching method was applied by comparing the applicants' scores against the company's ideal profile in three assessment aspects: administrative aspects, interview results, and skills covering nine criteria. The difference (gap) is converted into weights, grouped into core factors (60%) and secondary factors (40%), and then accumulated into a final score with inter-aspect weights of 35%, 25%, and 40%. Testing on five samples of programme position candidates resulted in a final ranking with the highest score of 3.52. The research results indicate that the developed system is capable of making the selection process more efficient, objective, and measurable
Model Hybrid Rule-Based dan KNN untuk Kecocokan Ikan Hias Terhadap Kualitas Air Muhamad Iqbal Nurdin Siregar; Aniq Astofa
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 04 (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.v7i04.2034

Abstract

The ornamental fish farming industry requires consistent water quality management, yet PT Ikan Bangun Indonesia still relies on manual expert judgment to determine fish-water compatibility, making the process subjective and prone to delayed detection of critical conditions. This research develops a web-based decision support system that combines a Rule-Based System using Forward Chaining as a lethal-condition filter (Layer I) with a K-Nearest Neighbor algorithm based on Euclidean distance as a suitability ranking layer (Layer II), covering ten ornamental fish species and seven water quality parameters. The system was built using a three-tier architecture with React.js, Laravel, PostgreSQL, and a Python FastAPI microservice, and was evaluated through Black Box Testing, White Box Testing using basis path testing, and a Confusion Matrix approach. The results show that all functional scenarios passed testing, all internal logical paths executed correctly, and the hybrid model classified water feasibility with 100% accuracy, precision, and recall. This study concludes that the hybrid Rule-Based and KNN approach is effective and reliable for automated decision-making in ornamental fish aquaculture
Sistem Pendukung Keputusan Pemilihan SMK Bagi Siswa SMP menggunakan Moora Kurnianto Octian Junaidi; Yuda Samudra
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 04 (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.v7i04.2037

Abstract

Choosing a Vocational High School is an important decision for junior high school students because interests, academic ability, economic conditions, and school characteristics must be considered simultaneously. At SMPN 3 Pasar Kemis, school information remains scattered and students' choices are often influenced by parents or peers, making the process insufficiently objective. This study develops a web-based Decision Support System using the Multi-Objective Optimization on the Basis of Ratio Analysis method. The system was developed using Agile, while requirements were collected through observation and interviews. Twenty vocational-school alternatives were assessed using eight criteria: field suitability, major suitability, distance, cost, interest and extracurricular compatibility, accreditation, academic score, and facilities. MOORA was implemented through decision-matrix construction, normalization, weighting, optimization-value calculation, and ranking. In the Student A simulation, SMKN 6 Tangerang achieved the highest score of 0.3875. Five scenarios showed that recommendations changed according to student characteristics and weighting profiles. All 71 black-box testing scenarios passed, while validation against Microsoft Excel produced a difference of 0.0000 for every alternative. The system can therefore support a more structured, transparent, and data-driven vocational-school selection process.
Analisis Sentimen Ulasan Aplikasi Astro Menggunakan Algoritma Naïve Bayes Ramdhan Ridho Saputra; Fitriana Destiawati; Bayu Ardiansyah
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 04 (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.v7i04.2160

Abstract

The use of online shopping applications generates many reviews that record user experiences after using the service. In the Astro application, reviews on the Google Play Store can be processed into information about user rating trends without having to manually examine each comment. This research develops sentiment analysis on Astro reviews using Naïve Bayes. A total of 1,000 reviews were collected as a dataset, and then the text was normalized through case folding, tokenizing, stopword removal, and stemming. The preprocessed text is represented using TF-IDF before being used in the model training and testing process. The dataset is divided into 80% training data and 20% testing data. The testing resulted in an accuracy of 88.89%, with stronger performance on positive and negative sentiments compared to neutral ones. These findings indicate that TF-IDF processing and Naïve Bayes can be used to map the opinion trends of Astro users and assist in presenting evaluative information regarding the application services.
Analisis Sentimen Terhadap Ulasan Aplikasi iPusnas di Google Play Store dengan Metode Indobert Ariestio Dava Pratama; Hendro Purwoko; Maria Cleopatra
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 04 (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.v7i04.2179

Abstract

The number of user reviews for the iPusnas application on the Google Play Store continues to increase, making the manual sentiment analysis process difficult. In addition, traditional classification methods still have limitations in understanding the context of the Indonesian language, especially in reviews that use informal language. This study aims to analyze the sentiment of user reviews of the iPusnas application using the IndoBERT method. The research data was obtained through web scraping using Python and resulted in 3,513 reviews from January 2021 to May 2026. The research stages include text preprocessing, sentiment labeling using the pretrained RoBERTa model, splitting the data into training and testing sets, data augmentation, classification using IndoBERT, and model evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The test results show that the model achieved an accuracy of 0.79. The best F1-score was obtained in the negative sentiment class at 0.85, while the neutral and positive classes each received a score of 0.70. The research results indicate that the IndoBERT method is capable of classifying the sentiment of iPusnas application reviews well and can serve as an evaluation tool for the National Library of the Republic of Indonesia to improve the quality of iPusnas application services.
Komparasi CNN-BiLSTM, CRNN-BIGRU-Attention, dan Emotion2vec+ untuk Deteksi Emosi Suara Pelanggan Muhammad Revin Putra Fahlefi; Bayu Sulistiyanto Ipung Sutejo; Riya Widayanti; Adi Widiantono
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 04 (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.v7i04.2223

Abstract

Speech emotion recognition can support customer service by identifying users’ emotional states more quickly and consistently. This study compares CNN-BiLSTM, CRNN-BiGRU-Attention, and emotion2vec+ in classifying anger, sadness, happy, and neutral. The data comprise 5,764 selected recordings from RAVDESS and CREMA-D, processed separately using a speaker-independent split. CNN-BiLSTM and CRNN-BiGRU-Attention were trained from scratch using log-mel spectrograms, whereas emotion2vec+ performed direct inference on raw waveforms without fine-tuning. The evaluation covered clean audio, babble noise and white noise at SNR levels of 15, 10, and 5 dB, cross-corpus testing, false negative rate (FNR) for anger and sadness, and computational efficiency. emotion2vec+ achieved the highest clean-data Unweighted Average Recall (UAR), reaching 93.49% on RAVDESS and 91.52% on CREMA-D, average noisy-condition UARs of 82.68% and 71.67%, and maximum critical-emotion FNRs of 9.38% and 14.29%, respectively. The highest cross-corpus UAR was only 33.69%, indicating limited cross-corpus generalization. CRNN-BiGRU-Attention was the most efficient model, with 1,692,997 parameters, a 6.47 MB weight file, and an inference time of 1.3805-1.6569 milliseconds per sample. Thus, emotion2vec+ offered superior classification performance and noise robustness, whereas CRNN-BiGRU-Attention was preferable when computational efficiency was prioritized.
Sistem Pakar Diagnosa Penyakit Kulit Menggunakan Metode Forward Chaining dan Certainty Factor Hera Syakira; Mercy Hermawati; Nurul Frijuniarsi
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 04 (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.v7i04.2258

Abstract

Skin diseases have a variety of symptoms and often show similarities, making it difficult for the public to recognize the type of disease and determine the appropriate initial steps. This research aims to design and build an expert system for diagnosing skin diseases in humans at the Restu Ibu Clinic by combining the Forward Chaining and Certainty Factor methods. Forward chaining is used to trace facts in the form of symptoms and match them with IF-THEN rules in the knowledge base, while the certainty factor is used to calculate the confidence level of the diagnosis based on the user's confidence value and expert weight. Data collection was carried out through observation, interviews with medical staff, and literature study. The knowledge base consists of 10 skin diseases, 42 symptoms, and 17 rules. The system is developed using PHP and MySQL and provides features for managing diseases, symptoms, rules, accounts, consultations, consultation history, and result printing. Testing on five case studies produced confidence values of 97.05% for psoriasis, 99.55% for atopic dermatitis, 98.53% for urticaria, 99.11% for tinea, and 100% for scabies. The values represent the confidence level of the inference results, not a measure of clinical accuracy. Black Box testing on 59 scenarios showed that all functions operated as expected. The system can be used as an initial screening aid and information support before further examination by medical personnel.
Analisis Penerimaan Aplikasi Klinisia Menggunakan Integrasi TAM dan UTAUT2 I Putu Prima Ananda; I Made Agus Oka Gunawan; Gede Indrawan; I Kadek Nicko Iswara Sudanta
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 04 (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.v7i04.2311

Abstract

The application of digital technology in healthcare services continues to grow through the provision of online patient registration services. UPTD Puskesmas Banjarangkan I uses the KLINISIA application to improve the efficiency of the registration process while reducing patient waiting times. Nevertheless, public acceptance regarding the use of this application is still minimally explored empirically. This study focuses on analyzing the effect of Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Facilitating Conditions (FC), Social Influence (SI), Hedonic Motivation (HM), and Price Value (PV) on Behavioral Intention in using the KLINISIA application through the integration of the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). A quantitative approach was used by distributing questionnaires to 200 respondents. A total of 180 prospective user data were analyzed using the multiple linear regression method. The test results show that all independent variables simultaneously have a significant effect on Behavioral Intention (F = 138.803; p < 0.001). Partially, only PU and FC provide a positive and significant influence, with FC being the strongest factor. In conclusion, perceived usefulness and the availability of supporting facilities are the main determinants motivating the public's intention to use the KLINISIA application.
Komparasi Naive Bayes dan Support Vector Machine dalam Mendeteksi Sarkasme Teks Twitter (X) Syifa Azzahra; Bayu Sulistiyanto Ipung Sutejo; Diah Aryani; Jefry Sunupurwa Asri
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 04 (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.v7i04.2326

Abstract

The use of non-standard language on social media has become one of the challenges in detecting sarcasm because the meaning conveyed is not always recognizable based solely on the words present in the text. This study aims to compare the performance of Naive Bayes and Support Vector Machine (SVM) in detecting sarcasm in non-standard Indonesian Twitter text (X). The Wilcoxon Signed-Rank Test is used to test the significance of the performance difference between the two algorithms. The audit results in 15,072 unique tweets, consisting of 12,057 training data and 3,015 test data. The test results show that Naive Bayes achieved an accuracy of 69.45%, precision of 67.66%, recall of 64.19%, and F1-Score of 65.88%, while SVM achieved an accuracy of 68.39%, precision of 66.51%, recall of 62.82%, and F1-Score of 64.61%. In the 5-fold cross-validation, Naive Bayes produced an average accuracy of 68.74% with a standard deviation of ±0.33%, while SVM achieved 68.17% with a standard deviation of ±1.14%. The Wilcoxon Signed-Rank Test results in a p-value of 0.4375 (>0.05), indicating that the performance difference between the two algorithms is not statistically significant. Thus, Naive Bayes shows slightly higher evaluation results numerically, but it cannot yet be stated that it has significantly superior performance compared to SVM on this research dataset.
Analisis Sentimen Pengalaman Pengguna Twitter Terhadap ChatGPT Menggunakan CNN dan LSTM Adam Mohammad Nur; Bayu Sulistiyanto Ipung Sutejo; Gerry Firmansyah; Budi Tjahjono
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 04 (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.v7i04.2333

Abstract

The development of generative artificial intelligence, particularly ChatGPT, has elicited various responses from users on the social media platform Twitter (X), including support, criticism, concerns, and neutral information. This research aims to analyze Twitter (X) users' sentiments toward ChatGPT and compare the performance of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models in classifying negative, neutral, and positive sentiments. The research uses a quantitative approach based on deep learning. Data were collected using the keywords "ChatGPT" and "Chat GPT." From the 11,210 tweets obtained, 10,586 were used after the cleansing process, which involved removing URLs, mentions, hashtags, emojis, symbols, punctuation, special characters, and excessive spaces. The data distribution consists of 6,281 negative tweets, 3,484 neutral, and 821 positive. The data was divided with an 80:20 ratio into 8,468 training data and 2,118 test data. Evaluation used accuracy, precision, recall, and F1-score. CNN achieved an accuracy of 69.55%, a macro F1-score of 57.17%, and a weighted F1-score of 69.36%, while LSTM achieved an accuracy of 72.29%, a macro F1-score of 62.07%, and a weighted F1-score of 72.78%. LSTM produced 1,531 correct predictions, while CNN produced 1,473. Based on these results, LSTM shows better performance in classifying sentiment toward ChatGPT.

Page 9 of 10 | Total Record : 92

 6   7   8   9   10 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 7 No. 04 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 03 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 02 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 01 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)