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RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Published by Universitas Abdurrab
ISSN : 24772062     EISSN : 2502891X     DOI : https://doi.org/10.36341/rabit
Core Subject : Science, Engineering,
Computer Science & IT Engineering
This journal is called RABIT, where the name comes from two words namely, RAB which means Abdurrab University and IT which means information technology, it can be interpreted as a journal of this journal Journal of Informatics Engineering Study Program Pekanbaru Abdurrab University. This RABIT journal contains various sciences related to the world of computers especially information technology and information systems, namely, this journal is published twice a year where the initial publication is on January 10 while for the second issue which is on July 10.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 696 Documents
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KLASIFIKASI TELUR CACING BERBASIS GAMBAR MENGGUNAKAN JARINGAN SARAF KONVOLUSIONAL: IMAGE-BASED CLASSIFICATION OF HELMINTHS EGGS USING CONVOLUTIONAL NEURAL NETWORKS Luluk Elvitaria Elvitaria; Ira Puspita Sari; Tengku Imam Buchari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6207

Abstract

This research aims to develop an image processing-based classification system for helminths eggs using Convolutional Neural Network (CNN) with a transfer learning and finetuning approach. Helminths eggs are important indicators in the diagnosis of helminth diseases in humans. However, the manual classification of helminths eggs requires significant time and effort. Therefore, an automated system that can classify helminths eggs with high accuracy would be highly beneficial in the diagnosis of these diseases. In this study, experiments were conducted using three CNN architectures that have proven effective in image classification tasks, namely EfficientNetB0, MobileNetV3, and ResNet50. Transfer learning method was employed by utilizing pre-trained models on large-scale image datasets. Subsequently, fine tuning was performed on the last layers of the models to adapt them to the helminths egg data. Testing was conducted using a dataset of helminths eggs collected from IEEE Dataport. The experimental results show that all three CNN architectures were able to classify helminths eggs with high accuracy, with EfficientNet-B0 achieving the highest accuracy (95.36%). The developed system in this study has the potential to be used in the efficient and accurate diagnosis of helminth diseases.
TRANSFORMASI DIGITAL UMKM MELALUI APLIKASI JOKER (JASA ORDER KETIKA MAGER) Liza Trisnawati; Salamun Salamun; Muhammad Roby
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6208

Abstract

Technological progress demands that all human work which in daily life still uses manual methods and whose performance is deemed less effective can be done with advanced technology. The food ordering process is generally carried out directly and via media such as WhatsApp, where customers contact restaurant staff regarding the available menu, the price of each food, the delivery process and payment. This ordering process takes more time, so the ordering process becomes less effective, because the officer has to reply to messages one by one to each customer regarding price, available stock, and delivery process. Therefore, creating ordering media in the form of an application can be a solution to overcome deficiencies and be able to mediate both the ordering process and product delivery. Data collection took the form of literature studies, interviews and observations. Quantitative methods are used in this research process to be used in conducting testing. The joker application was tested using black box testing successfully and a User Acceptance Test (UAT) was carried out by obtaining an average score of 92.48% from 1,000 customer orders with the title "Very Good" which means customers UMKM with this joker application. The results of this research prove that the mobile-based Joker application can be used to facilitate UMKM sales.
OPTIMASI NILAI K PADA KNN DENGAN INTEGRASI SIMULATED ANNEALING DAN HILL CLIMBING UNTUK PREDIKSI HARGA RUMAH Rudy Cahyadi; Ariesta Damayanti
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6214

Abstract

The K-Nearest Neighbors (KNN) algorithm is a widely adopted method for classification and regression due to its straightforward implementation. However, KNN’s performance is heavily influenced by the choice of the parameter k (the number of nearest neighbors), where a non-optimal k value may lead to overfitting or underfitting. To address this challenge, this study proposes an optimization approach for the k parameter using two local search algorithms: Simulated Annealing (SA) and Hill Climbing (HC). The dataset utilized is the Real Estate Dataset from Kaggle, which contains housing characteristics, with the prediction target being MEDV (Median Value of Owner-Occupied Homes). This research compares the performance of standard KNN, KNN optimized with SA, and KNN optimized with HC. Model evaluation is conducted using the Root Mean Square Error (RMSE) metric. Experimental results demonstrate that both SA and HC significantly enhance prediction accuracy compared to standard KNN. SA excels in exploring the solution space and avoiding local optima, while HC offers faster convergence. The findings reveal that KNN with the optimal k achieves an RMSE of 3.3159 with k=2. The integration of both methods demonstrates potential for determining a more optimal and stable k value across various regression datasets.
ENKRIPSI PESAN CHAT MENGGUNAKAN ALGORITMA CHACHA20 PADA APLIKASI KOMUNIKASI REAL-TIME Dandi Darmansyah; Abdul Halim Hasugian
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6220

Abstract

The advancement of communication technology has now become a crucial component in supporting the flow of information in society. With the increasing use of real-time applications, understanding the security of messages transmitted over the internet is essential to protect them from eavesdropping and data theft. The application of encryption is an initial step to enhance the security of digital communication. This study aims to implement the ChaCha20 algorithm to encrypt text messages in a communication application based on Android using the Flutter framework and Firebase backend service. Test results indicate a significant performance difference compared to the AES-CBC algorithm, where ChaCha20 offers faster encryption and decryption times as well as higher throughput. Based on these findings, the use of ChaCha20 is recommended for applications requiring high-speed and efficient data exchange. The impact of this research is expected to raise awareness and understanding of the importance of selecting the right cryptographic algorithm in building secure communication systems. Furthermore, this research is anticipated to contribute meaningfully to the development of robust communication applications against growing cybersecurity threats, especially on mobile devices.
SISTEM PAKAR HYBRID BERBASIS ANDROID DENGAN OPTIMASI CERTAINTY FACTOR UNTUK DIAGNOSIS PENYAKIT MENULAR DI FASILITAS KESEHATAN TERBATAS Samsul Samsul; Abd. Ghofur; Farihin Lazim
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6238

Abstract

Infectious diseases such as Dengue Fever (DHF), Tuberculosis (TB), and Malaria are still major health problems in Indonesia, especially in areas with limited medical resources such as Banyuputih Health Center. This study aims to develop an Android-based expert system that applies the Certainty Factor (CF) method to assist the diagnosis process of infectious diseases. The system is designed to calculate the level of confidence in a diagnosis based on symptoms inputted by the user, which are then combined with the confidence value of medical experts. There are 68 symptoms from 3 diseases with testing of 50 simulation cases used in this system, which were obtained through an interview process with medical personnel. Each symptom is given a CF value weight, which is then calculated in stages to produce the final diagnosis value. Testing was carried out using simulation data, and the implementation results showed that the system was able to provide high diagnostic accuracy, with a CF value of 0.9975 (99.75%) for Malaria cases, 0.997 (99.7%) for Tuberculosis, and 0.994 (99.4%) for DHF. The use of this system is expected to accelerate the diagnosis process, reduce the workload of medical personnel, and increase the efficiency of health services. This research contributes to the use of information technology as a supporting solution for digital transformation in the primary health care sector.
SISTEM INFORMASI INVENTORY PADA CV. SAMUDRA LAUTAN BERKAT PASURUAN MENGGUNAKAN METODE SCRUM Mochamad Ramadhani; Lambang Probo Sumirat; Slamet Kacung
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6259

Abstract

This study aims to design a web-based inventory information system at CV. Samudra Lautan Berkat Pasuruan to improve the efficiency and accuracy of stock management. The previous manual process using Microsoft Excel was considered ineffective and prone to errors. The system was developed using the Agile Scrum methodology which allows for structured and flexible development. This study uses a descriptive approach with data collection through observation, interviews, and literature studies. The system is built using PHP and MySQL, and applies the FIFO method for stock management. The features developed include recording incoming and outgoing goods, reports, and stock monitoring by admins, warehouses, and leaders. The implementation results show that this system accelerates the recording and reporting process, minimizes human error, and supports more accurate decision making in inventory management.
ANALISIS FAKTOR RISIKO SINDROM OVARIUM POLIKISTIK (PCOS) PADA WANITA USIA SUBUR MENGGUNAKAN ALGORITMA EXPLAINABLE AI: POHON KEPUTUSAN VS RANDOM FOREST BERBASIS DATA KLINIS: RISK FACTOR ANALYSIS OF POLYCYSTIC OVARY SYNDROME (PCOS) IN WOMEN OF REPRODUCTIVE AGE USING EXPLAINABLE AI ALGORITHMS: DECISION TREE VS RANDOM FOREST BASED ON CLINICAL DATA Muhammad Syaifur Rohman; Tsalisa Noor Rahmawati
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6267

Abstract

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age and is a major cause of infertility and metabolic disturbances. This study aims to analyze risk factors of PCOS using Decision Tree and Random Forest classification algorithms based on clinical data. The dataset consists of 541 female patient records with 11 clinical features including age, body mass index (BMI), hormonal levels, and physical symptoms such as hair growth and pimples. The models were evaluated using accuracy, precision, recall, and f1-score. The Decision Tree model achieved an accuracy of 83.33%, with hair growth and weight gain as dominant features, while the Random Forest model achieved 82.41% accuracy and showed better performance in detecting positive cases (recall = 63.64%). The findings highlight that a combination of hyperandrogenism symptoms and hormonal indicators are key predictors of PCOS. This study demonstrates that machine learning algorithms can serve as effective tools to support early diagnosis of PCOS using clinical data
DETEKSI KUALITAS RUMPUT LAUT MENGGUNAKAN METODE CONVELUTION NEURAL NETWORK (CNN) BERDASARKAN CITRA DIGITAL (STUDI KASUS : DESA ALASMALANG KECAMATAN RAAS SUMENEP) Supandi Supandi; Abd. Ghofur; Firman Santoso
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6273

Abstract

Seaweed quality greatly determines the economic value and competitiveness of processed marine products. To assist the automatic and objective quality classification process, a Convolutional Neural Network (CNN)-based approach is used with RGB color features from digital images as the main input. Seaweed images go through the stages of preprocessing, RGB color feature extraction, quality class labeling, and CNN model training. Two quality categories are defined, namely good and bad. The training results show that the CNN model is able to classify seaweed quality with an accuracy rate of 92% on training data and 91.19% on test data, as well as low and stable loss values. The application of CNN to color features has proven effective for image-based seaweed quality classification, and can be further developed in an automatic quality evaluation system in the agricultural and marine sectors.
ANALISIS PENERAPAN D’JOS DI PROVINSI DKI JAKARTA DENGAN PENDEKATAN TECHNOLOGY ACCEPTENCE MODEL (TAM) STUDI KASUS BPKD: ANALYSIS OF THE IMPLEMENTATION OF D’JOS IN DKI JAKARTA PROVINCE USING THE TECHNOLOGY ACCEPTANCE MODEL (TAM): A CASE STUDY AT THE REGIONAL FINANCIAL MANAGEMENT AGENCY (BPKD) Achmad Farhan; Ifan Junaedi; Anton Zulkarnain Sianipar
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6298

Abstract

This study analyzes the implementation of the Deposito Jakarta Online System (D’JOS) application at the Regional Financial Management Agency (BPKD) of DKI Jakarta Province as part of efforts to digitalize regional financial management based on the principles of Good Governance. The approach used is descriptive qualitative, with data collected through interviews, direct observations, and document studies. The analysis is conducted using the Technology Acceptance Model (TAM) framework, which includes five main aspects: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), Behavioral Intention to Use (BI), and Actual Use (AU). The results indicate that the D’JOS application is perceived as beneficial in improving the efficiency, accuracy, and transparency of deposit management. A total of 85% of users stated that D’JOS has facilitated their work, particularly in financial reporting and monitoring. Users also reported ease of use and expressed positive attitudes and strong intentions to continue using the system sustainably. In practice, the system has been consistently utilized in BPKD’s daily operational activities. Therefore, D’JOS has proven to support digital transformation in regional financial governance and has the potential to be replicated by other local governments. This study recommends the development of additional features such as automatic notifications, stronger data security, and system integration, as well as the establishment of specific regulations, such as a Governor's Decree, to support the standardization and sustainability of D’JOS usage within the DKI Jakarta Provincial Government.
ANALISIS SENTIMEN PUBLIK DI MEDIA SOSIAL TERHADAP KASUS DUGAAN KORUPSI IMPOR MINYAK PERTAMINA MENGGUNAKAN XGBOOST Dhea Ferdiana Merpatika; Albert Yakobus Chandra
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6307

Abstract

Public sentiment analysis of the alleged corruption case of oil imports by PT Pertamina was carried out using a machine learning approach using the Extreme Gradient Boosting (XGBoost) algorithm. Data was obtained from user comments on Pertamina's official accounts on social media platforms X, Instagram, and TikTok during the period from February 1, 2025 to March 31, 2025. Comments collected through the scraping process were then processed through text preprocessing stages such as normalization, tokenization, filtering, and stemming. Feature representation was carried out using the Term Frequency-Inverse Document Frequency (TF-IDF) method to convert text data into numeric form. Each comment was manually labeled with sentiment into three categories, namely negative (0), neutral (1), and positive (2). The XGBoost model was trained with TF-IDF extracted data and evaluated using metrics such as accuracy, precision, recall, and f1-score. The evaluation results showed that the model was able to classify sentiment with good performance, with an accuracy value of 78% and an F1-score of 78%. The application of this method shows the effectiveness of the machine learning approach in understanding public perception of strategic issues in the national energy sector. The findings show that only 8% of positive sentiment on platform X, Instagram and TikTok indicates a crisis of public trust that needs to be responded to by Pertamina.

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