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Infotek : Jurnal Informatika dan Teknologi
Published by Universitas Hamzanwadi
ISSN : 26148773     EISSN : 26148773     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
INFOTEK Jurnal Informatika dan Teknologi Fakultas Teknik Universitas Hamzanwadi selanjutnya disebut Jurnal Infotek (e-ISSN: 2614-8773) merupakan Jurnal yang dikelola oleh Fakultas Teknik Universitas Hamzanwadi yang mempublikasikan artikel ilmiah hasil penelitian atau kajian teoritis (invited authors) dalam bidang (1) keilmuan informatika, (2) Rekayasa Perangkat Lunak, (3) Multimedia, (4) Jaringan Komputer, (5) Data Mining, (6) Image Processing, (7) Komputer Vision, (8) Mikrokontroller, (9) Robotik, (10) IOT yang belum pernah dipublikasikan. Jurnal Infotek diterbitkan oleh Fakultas Teknik Universitas Hamzanwadi dua kali setahun yaitu pada bulan Januari dan Juli. Jurnal Infotek Telah Terindeks pada Google Scholar.
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 458 Documents
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Perbandingan SVM dan Logistic Regression untuk Klasifikasi Teks Konsultasi Daring Kehamilan dan Menstruasi Zahra Syifa Prasasti; Safitri Juanita
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35011

Abstract

The high volume of doctors' response texts in online health consultations, particularly on pregnancy and menstruation topics, has increased the need for an accurate and automated medical text classification system. However, these two topics often share similar medical terminology and consultation contexts, making the classification process challenging. This study aims to develop an automatic classification model for Indonesian-language doctors' responses in online health consultations by comparing two algorithms, namely Support Vector Machine (SVM) and Logistic Regression (LR), which were selected because they are widely used in text classification tasks with TF-IDF feature representation and are capable of handling high-dimensional data. The contributions of this study include the development of a classification model using an underexplored Indonesian doctors' response dataset, feature importance analysis to identify dominant medical terms in each category, and a comparative evaluation of SVM and LR performance validated using McNemar's statistical test. The study utilized the public "Doctor's Answer Text Dataset in Indonesian" from Mendeley Data, comprising 17,807 records. The models were developed using the CRISP-DM framework with TF-IDF feature extraction and evaluated across four train-test split ratios using accuracy, precision, recall, and F1-score metrics. The results showed that Logistic Regression (LR) consistently outperformed Support Vector Machine (SVM), achieving the highest accuracy of 0.9359 with an 80:20 train-test split. Feature importance analysis identified "hamil" (pregnant) and "janin" (fetus) as the dominant features for the pregnancy category, and "menstruasi" (menstruation) and "siklus" (cycle) for the menstruation category. These findings demonstrate that TF-IDF-based LR is an effective approach for classifying Indonesian-language online health consultation texts.
Sistem Pemantauan Pelacak Panel Surya Berbasis Internet of Things dengan Aplikasi Ponsel Pintar Hadian Mandala Putra; Habibul Muzzamil; Ida Wahidah; Suhartini
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35038

Abstract

Solar energy is an environmentally friendly renewable energy source that has gained increasing attention due to its sustainability and widespread availability. The use of a solar tracking system capable of following the sun's movement can significantly improve the performance of solar panel systems. This study aims to develop an Internet of Things (IoT)-based monitoring system utilizing a smartphone application (Telegram on Android) to monitor and control a solar panel tracking system. The research employed a Research and Development (R&D) approach. The hardware components consisted of a tracking motor, Light Dependent Resistor (LDR) sensors, and a DC voltage sensor integrated with a solar panel system. The software component utilized a Telegram Bot as a platform for remote monitoring and control. The results demonstrated that the developed system was capable of performing real-time monitoring and control of the solar panel system through an internet connection. The main contribution of this study lies in the integration of an LDR-based solar tracking mechanism with a Telegram Bot-based monitoring and control system. Experimental results indicated that the proposed solar tracking system achieved a 23.18 % increase in average output voltage compared to a conventional static solar panel system. Furthermore, the output voltage remained more stable throughout the day because the solar panel was able to automatically follow the sun's trajectory. These findings indicate that the proposed system can enhance solar energy harvesting performance while providing a practical solution for remote monitoring and control.
Implementasi Metode Rule-Based Scoring Pada Sistem Penilaian Resiko Penyakit Diabetes Dan Hipertensi Pada Pasien Umum Di Puskesmas Berbasis Web Armando Tegar Hedonio; Sri Widoyoningrum
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35050

Abstract

Health screening procedures at primary healthcare facilities play a crucial role in early disease risk detection. However, the screening process at the Jelakombo Community Health Center is still performed manually, resulting in inadequate data recording and risk assessment. This study aims to design and implement a web-based disease risk assessment system for general patients using the Rule-Based Scoring method. The system was developed using the Laravel framework and a MySQL database, utilizing a digital questionnaire as a data collection medium. The Rule-Based Scoring method was used to calculate a total score based on user responses and classify risk levels into low, medium, and high categories. The results showed that the system was successfully implemented and all main functions of the system ran as required based on Black Box Testing. The system is capable of managing patient data, filling out questionnaires, automatically calculating risk scores, and displaying risk classification results digitally. The developed system can be used as an initial screening tool to support the early detection of disease risks in general patients at the Jelakombo Community Health Center.
Perbandingan Penggunaan Arsitektur Graph GCN Dan LightGCN Pada Sistem Rekomendasi Hotel Berbasis Rating Pengguna Dengan Dataset Terbatas Rhodil Fauzi; Abu Tholib; Ahmad Hudawi AS
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35058

Abstract

Hotel recommendation systems often fail to recommend new hotels due to extreme data sparsity problems (item cold-start) and are vulnerable to the computational over-smoothing phenomenon. This study aims to comprehensively evaluate and compare the ranking architectures of Graph Convolutional Network (GCN) and LightGCN. The method used is a computational experiment using an Ablation Study approach to dissect the effect of propagation depth (1-Layer vs. Multi-Layer) and the injection of external features. The evaluation was conducted on a small-sized dataset from Mendeley Data containing review histories for 16 hotel entities. The main results show that the 1-Layer LightGCN with features is the most superior model for active users (warm-start), achieving an NDCG@10 score of 0.8702. However, in the extreme new hotel scenario (0-shot cold-start), this shallow architecture failed, and the best solution was actually won by the pure Multi-Layer model without features (No-Feature), which achieved a Hit Ratio (HR@10) of 71.43%. In conclusion, there is no single perfect model for all conditions; system implementation is recommended to adopt a dual-framework that integrates the speed of 1-Layer LightGCN and the propagation robustness of the Multi-Layer model.
Rancang Bangun Password Manager Berbasis Web Menggunakan Framework Django Dan Enkripsi Fernet Widarmawati Waruwu; Yusnia Budiarti; Mutiara Nazwah; Angga Wibowo Saputro; Diwan Mardianus Laia
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35065

Abstract

Most prior password manager studies rely on basic encryption without strong key derivation and have not consistently implemented a zero-knowledge architecture, leaving encrypted user data potentially accessible to service providers or third parties. This research contributes by designing and building a web-based password manager that integrates three security layers within a unified Django ecosystem: PBKDF2 key derivation with 100,000 SHA-256 iterations, hardware-based unique random salt per user (os.urandom), and authenticated Fernet encryption (AES-128-CBC + HMAC-SHA256). The novelty of this research lies in the strict enforcement of the zero-knowledge principle, where the master password is never stored in the database, making credentials inaccessible even to system administrators. The combination of personalized salt and PBKDF2 also ensures that two users sharing an identical master password always produce distinct encryption keys — a feature not found in existing Django-based implementations. Black Box Testing across ten functional and security scenarios yielded a 100% success rate, covering CRUD operations, unauthorized access rejection, ciphertext tampering detection, and cross-user salt uniqueness verification. The results demonstrate that integrating PBKDF2, unique salt, and Fernet within the Django framework produces a transparent, secure credential storage system resilient to brute force attacks, rainbow table attacks, and database-level data manipulation.
Perbandingan Algoritma Machine Learning untuk Klasifikasi Diabetes Menggunakan Feature Selection dan Hyperparameter Tuning Fajar Maula Hidayat; Hafidz Sanjaya
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35199

Abstract

Diabetes mellitus is a chronic disease with an increasing prevalence worldwide, requiring early detection to support faster and more accurate disease management. This study aims to compare the performance of several machine learning algorithms for diabetes classification using feature selection and hyperparameter tuning. The dataset used was the Pima Indians Diabetes Dataset obtained from the Kaggle platform. The research consisted of data preprocessing, feature selection using SelectKBest, training and testing data splitting, hyperparameter tuning using GridSearchCV, and model evaluation using accuracy, precision, recall, F1-score, ROC-AUC, and cross validation. The evaluated algorithms included Logistic Regression, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), and Naive Bayes. The results showed that the KNN algorithm achieved the best performance with an accuracy of 74.02%, precision of 63.46%, recall of 61.11%, F1-score of 62.26%, and ROC-AUC of 79.60%. The findings indicate that integrating data preprocessing, feature selection, hyperparameter tuning, and cross validation provides a more comprehensive evaluation process for machine learning models in diabetes classification.
Deteksi Hoaks Berita Berbahasa Indonesia Menggunakan IndoBERT dengan Penanganan Ketidakseimbangan Kelas Berbasis Gabungan Class Weighting dan Focal Loss Riadhul Muttaqin; Muhammad Edya Rosadi; Muhammad Iqbal Firdaus; Dian Agustini
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35214

Abstract

The spread of hoaxes in Indonesian-language digital media has risen sharply in the past five years with wide societal harm. Most prior work on Indonesian hoax detection emphasizes model architecture, while the class-imbalance problem in field data receives less attention. This study presents a data-centric approach combining the IndoBERT pretrained language model with a hybrid class-imbalance objective of class weighting and focal loss. The dataset is the indonesiafalsenews corpus of 4231 labelled articles with an approximate 4.5 to 1 hoax-to-fact ratio. Evaluation uses five-seed runs with McNemar and paired-bootstrap significance testing. The proposed configuration achieves an F1-macro of 0.7240, accuracy of 0.8392, ROC-AUC of 0.8083, and a Matthews correlation coefficient of 0.4510, significantly outperforming every TF-IDF baseline at the 0.05 level. Text augmentation does not provide consistent gains, which implies that imbalance handling is the more effective lever for Indonesian hoax detection.
Implementasi Retrieval-Augmented Generation dan Semantic Search pada Chatbot Artificial Intelligence Berbasis Web untuk Optimalisasi Layanan Akademik Muhammad Saiful; L M Samsu; Imam Fathurrahman; Amri Muliawan Nur
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35253

Abstract

Academic information services in higher education institutions still face various obstacles, such as delays in information delivery, limited access to services, and high administrative burdens due to repetitive student inquiries. This study aims to implement Retrieval-Augmented Generation (RAG) and Semantic Search technology in a web-based Artificial Intelligence chatbot to optimize academic services at the Faculty of Engineering, Hamzanwadi University. The research method used is Design Science Research (DSR), which includes data collection, system requirements analysis, design, implementation, testing, and system evaluation. The chatbot's knowledge base is built from academic documents such as academic guidelines, service SOPs, academic calendars, scholarship information, and other administrative documents. The system was developed using an integration of LangChain, Azure OpenAI Service, Azure AI Search, FastAPI, Next.js, and Supabase. Semantic Search techniques are used to perform vector embedding-based searches, while RAG is utilized to generate contextual answers based on relevant documents. Test results show that all key system features performed well with a 100% success rate in unit testing. A user satisfaction evaluation using the Customer Satisfaction Index (CSI) method with 54 respondents yielded a score of 89.34%, categorized as "Very Satisfied," with a Mean Satisfaction Score above 4.37 on a maximum scale of 5.0. The AI ​​chatbot successfully addressed traditional academic information service issues by providing 24/7 service, reducing the workload of campus staff, and ensuring information consistency through RAG technology.
Rancang Bangun Sistem Penyiraman Tanaman Bibit Cabai Menggunakan Mikrokontroler Arduino L M Samsu; Muhammad Saiful; B Nadila Nuzululnisa; Amri Muliawan Nur
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35264

Abstract

Manual watering of chili seedlings requires considerable time and effort and is often inconsistent in the amount of water provided, which can affect plant growth. This study aims to design and implement a prototype of a chili seedling watering system based on the Arduino Uno microcontroller that can operate automatically and manually. The research method used is a design and development method, which includes observation, system design, device assembly, programming, and system testing. The system uses a soil moisture sensor as the main controller, a DHT11 sensor to monitor environmental temperature, a relay to control the water pump, a toggle switch for manual operation, and a 16x2 LCD to display soil moisture, temperature, and pump status in real time. The test results show that in automatic mode, the pump activates when the soil moisture is below the threshold of 56% and stops when the moisture reaches 60%. In manual mode, the pump can be controlled directly using the toggle switch, while the LCD successfully displays all system parameters properly. The contribution of this study is the development of a simple and easy-to-use chili seedling watering prototype that helps maintain soil moisture stability and reduces dependence on manual watering
Implementasi Algoritma K-Nearest Neighbor pada Sistem Deteksi Tingkat Dehidrasi Berdasarkan Warna Urin Berbasis IoT dan Android Jumawal; Mahpuz; Muhamad Sadali; Muhammad Wasil
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.35273

Abstract

Dehydration is a condition in which the body loses more fluids than it receives, potentially causing health problems and disrupting normal body functions. One indicator of dehydration can be observed through urine color, where darker urine indicates a higher level of dehydration. This study aims to implement the K-Nearest Neighbor (KNN) algorithm in an Internet of Things (IoT)- and Android-based dehydration detection system using urine color analysis. The system utilizes a TCS3200 color sensor to detect urine color, an ESP32 microcontroller to process and transmit data, and an Android application to display detection results in real time. The acquired color data are processed using the KNN algorithm to classify dehydration levels into several categories. The classification results are then displayed on the Android application along with hydration reminder notifications. System testing was conducted using 50 artificial urine color samples with varying color intensities. The results showed that the proposed system was able to classify dehydration levels with an accuracy of 92% and successfully display real-time detection results through the Android application. The developed system is expected to assist users in monitoring their hydration status easily and effectively.

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