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JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Published by STMIK Nusa Mandiri
ISSN : -     EISSN : 25274864     DOI : -
Core Subject : Science,
Computer Science & IT
Kegiatan menonton film merupakan salah satu cara sederhana untuk menghibur diri dari rasa gundah gulana ataupun melepas rasa lelah setelah melakukan aktivitas sehari-hari. Akan tetapi, karena berbagai alasan terkadang seseorang tidak ada waktu untuk menonton film di bioskop. Dengan bantuan media internet, berbagai macam aplikasi nonton film android sangat mudah dicari. Hanya bermodalkan smartphone saja para penonton film dapat streaming berbagai macam jenis film di mana saja dan kapan saja mereka inginkan. Akan tetapi, karena banyaknya pilihan aplikasi nonton film android yang bisa digunakan, terkadang seseorang bingung memilihnya. Untuk itu, diperlukan suatu sistem pendukung keputusan yang dapat digunakan para pengguna sebagai alat bantu pengambilan keputusan untuk memilih dengan berbagai macam kriteria yang ada. Salah satu metode yang digunakan adalah metode Analytical Hierarchy Process (AHP). AHP melakukan perankingan dengan melalui penjumlahan antara vector bobot dengan matrik keputusan dengan tujuan agar hasil yang diberikan lebih baik dalam menentukan alternatif yang akan dipilih. Berdasarkan hasil penelitian yang dilakukan oleh 36 sampel responden didapatkan kriteria konten menjadi prioritas pertama pengguna untuk memilih aplikasi nonton film android dengan nilai bobot sebesar 0,224. Sedangkan Netflix menjadi alternatif dengan prioritas pertama keputusan pengguna dalam memilih aplikasi nonton film android dengan nilai bobot sebesar 0,352.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 394 Documents
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THE ROLE OF AI (ARTIFICIAL INTELLIGENCE) FOR ALZHEIMER: A SYSTEMATIC REVIEW Ester Rumaseb; Sulistiyani Sulistiyani; Lalu Guntur Payasan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6154

Abstract

Alzheimer's disease (AD) is the most common type of dementia and represents a significant global health problem due to its profound impact on patients' quality of life and the heavy burden it places on health care. Alzheimer's is characterized by a progressive decline in cognitive function and memory, ultimately disrupting daily activities and leading to dependence on long-term care. This systematic literature review aims to explore the role of AI in diagnosing and managing Alzheimer’s disease. The method used in this study refers to the PICO framework to highlight various studies on the role of AI for Alzheimer's disease. Recent breakthroughs in the field of artificial intelligence (AI), particularly machine learning (ML) and deep learning, offer promising innovative approaches to improve diagnosis, monitoring, and understanding of Alzheimer's disease.
DESIGN OF CONTROL SYSTEM AND TEMPERATURE IN COFFEE DRYER ARDUINO BASED AUTOMATIC USING FUZZY Ratu Mutiara Siregar; Budi Mulyara; Rahmad Dian; Maisarah Maisarah; Muhammad Akbar Syahbana Pane; Andi Prayogi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6166

Abstract

The coffee bean drying process is a crucial stage in ensuring the final quality of coffee products. Conventional drying methods, which rely on sunlight, face several challenges, such as dependence on weather conditions and prolonged drying times. This study proposes the design of a control and temperature system for an automatic coffee dryer based on the Arduino Mega 2560, aimed at enhancing the efficiency and consistency of the drying process. The system utilizes a semi-enclosed drying technology equipped with DHT22 temperature and humidity sensors, controlled by Arduino-Uno and Fuzzy Logic. This control system monitors temperature and humidity in real-time, maintaining the drying conditions at 55°C and 15% RH. If the temperature or humidity exceeds the set limits, the system activates an LED and buzzer alarm, indicating that the drying process has reached optimal conditions. The prototype was tested under various conditions, and the results demonstrate that the system has a high accuracy level in controlling temperature and humidity, significantly accelerating the drying process compared to traditional methods. By implementing this technology, the coffee industry in Indonesia is expected to achieve the Coffee Drying Operational Standards in accordance with SNI, maintain flavor quality, optimize the use of drying land, and reduce drying duration. This development offers an innovative solution that can enhance the quality and productivity of coffee processing, providing significant economic benefits to farmers and coffee industry stakeholders.
OPTIMIZING THE KNN ALGORITHM FOR CLASSIFYING CHRONIC KIDNEY DISEASE USING GRIDSEARCHCV Muhammad Rahmansyah Siregar; Dedy Hartama; Solikhun Solikhun
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6214

Abstract

Chronic Kidney Disease (CKD) is a progressive condition that impairs kidney function and cannot be cured. Early detection is crucial for effective management and therapy. However, diagnosing CKD is challenging as patients often have comorbidities such as diabetes, hypertension, or heart disease, which complicate diagnosis and treatment. Accurate classification methods are essential for early detection. K-Nearest Neighbor (KNN) is a classification algorithm that groups data based on feature similarity. K-NN is an algorithm that is resistant to outliers, easy to implement, and highly adaptable. It only requires distance calculations between data points and does not involve complex parameters. However, its performance depends on hyperparameters such as the number of neighbors (k), weighting, and distance metric. Incorrect hyperparameter selection can lead to overfitting, underfitting, or reduced accuracy. To address these issues, GridSearchCV is used to optimize KNN by systematically selecting the best hyperparameters, ensuring improved accuracy and reduced overfitting. This optimization enhances the model’s reliability in early CKD detection compared to other methods. This study aims to determine the optimal KNN parameters for CKD classification using GridSearchCV. The results show 8.05% accuracy improvement and reduction in overfitting, with the prediction gap between training and testing decreasing from 6% to only 1.15%. These enhancements contribute to more reliable CKD diagnosis, enabling accurate early detection and better clinical decision-making.
DEEP BELIEF NETWORK (DBN) IMPLEMENTATION FOR MULTIMODAL CLASSIFICATION OF SENTIMENT ANALYSIS Hilmi Hibatullah; Aris Thobirin; Sugiyarto Surono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6257

Abstract

In sentiment analysis, the use of multimodal data, consisting of a combination of images and text, is becoming increasingly important for understanding digital context. However, the main challenge lies in effectively integrating these two types of data into a single learning model. Deep Belief Network (DBN), with its capability to learn hierarchical data representations, is utilized to explore optimal strategies for multimodal sentiment analysis. The dataset includes 34,034 images from the FERPlus dataset to train the model in classifying emotions based on facial expressions, as well as 999 text and image samples obtained through crawling X. Experiments were conducted by comparing the performance of DBN with 2, 3, and 4 hidden layers across different test data sizes (10%-50%). The results indicate that the 3-hidden-layer configuration achieved the best performance, with a highest accuracy of 76% at a 20% test data size. Additionally, testing different learning rates (10⁻⁴ to 10⁻⁷) produced consistent results, but the fastest computation time was achieved with a learning rate of 10⁻⁴. Based on these findings, DBN with a 3-hidden-layer configuration and a learning rate of 10⁻⁴ is considered a more efficient alternative for multimodal sentiment analysis based on text and images.
UEQ-BASED EVALUATION OF USER EXPERIENCE: A CASE STUDY ON ENGLISH READING WEBSITES DEVELOPMENT Argo Wibowo; Lemmuela Alvita Kurniawati; Susanti Malasari; Paulina Besty Fortinasari
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6264

Abstract

Technology has become essential in education, requiring specialized instructional media tailored to each subject. This study develops a web-based learning system designed specifically for English education, integrating both passive and interactive learning approaches to enhance engagement and effectiveness. This study contributes to showing that passive-interactive English learning websites can help users in both the advanced and non-advanced categories. However, before the system is publicly accessible, system testing focusing on user experience is important to achieve the intended learning objectives. The current study employed the User Experience Questionnaire (UEQ) framework, consisting of 26 questions across the categories of Attractiveness, Pragmatic, and Hedonic. The UEQ framework has been well-validated and widely used in various studies and professional industries for assessing user experience, ensuring that the user experience scores are objective. The findings showed that the system scored lowest in the “Hedonic” category due to a low “Novelty” score of 1.344, which was 31% lower than the highest score “Attractiveness” at 1.962. Interface that visually appealing and offers fresh, dynamic content, users are more likely to stay engaged.  However, the overall average score across the three UEQ categories for the English learning system was 1.823, indicating a good user experience.
THE IMPACT OF WORD EMBEDDING ON CYBERBULLYING DETECTION USING HYBIRD DEEP LEARNING CNN-BILSTM Moh. Hilman Fariz; Erwin Budi Setiawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6270

Abstract

Cyberbullying can be perpetrated by anyone, whether children or adults, with the primary aim of belittling or attacking specific individuals. Social media platforms like X (formerly Twitter) often serve as the primary medium for cyberbullying, where interactions frequently escalate into retaliatory attacks, intimidation, and insults. In detecting these actions, short tweets are often difficult to understand without context, making specialized approaches like word embedding important. This research uses GloVe feature expansion, utilizing a corpus generated from the IndoNews dataset containing 127,580 entries to enhance vocabulary understanding in tweets that include the use of Indonesian language in both formal and informal forms. This data was then classified using the Hybrid Deep Learning method, which combines Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with used 30,084 tweets taken from platform X as the dataset. The analysis results show that the application of expansion features using GloVe can improve the performance of the BiLSTM-CNN hybrid model, with the highest accuracy reaching 83.88%, an increase of +3.65% compared to the hybrid model without GloVe. This research successfully detected cyberbullying on platform X, making a significant contribution to efforts to create a safer and more positive social media environment for users.
INTELLIGENT SYSTEM TO DETERMINE THE BEST LECTURER USING ADDITIVE RATIO ASSESSMENT ALGORITHM Wahyudi Wahyudi; Budy Satria; Lutfil Khairi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6281

Abstract

The quality of a lecturer's performance is one of the keys to institutional success that must be continuously improved. The performance assessment of lecturers in the Informatics study program of the Faculty of Information Technology, Andalas University faces obstacles in processing quantitative and qualitative data so that it is vulnerable to subjectivity including research productivity, teaching effectiveness, contributions to community service and additional activities. In addition, limitations in a systematic evaluation system result in unfairness and lack of transparency in the decision-making process. The research objective is to create a technology-based approach by applying the Additive Ratio Assessment method based on a Decision Support System. The ARAS method was chosen because it is able to determine effective final results based on multiple criteria that have been determined. The application of the ARAS method consists of 5 stages, namely determining the decision matrix, normalizing the decision matrix, weighting the normalization results, determining the optimum function value and ranking results. The results obtained are alternative data consisting of A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,A11 and 8 criteria and weighting, namely the last education (10%), functional position (15%), certification (20%), number of publications (15%), author order (15%), publication index quality (10%), research grants (10%) and PkM (5%). The ranking results with the highest value in order 1-5 are 0.113875, 0.109785, 0.104235, 0.099005, 0.094715. The final conclusion of this research is that the ARAS method is able to prove the best lecturer assessment to be more efficient, transparent and subjective to be applied in the Andalas University Informatics study program.
PERFORM COMPARATION OF DEEP LEARNING METHODS IN GENDER CLASSIFICATION FROM FACIAL IMAGES Yosefina Finsensia Riti; Ryan Putranda Kristianto; Dionisius Reinaldo Ananda Setiawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.4717

Abstract

Identifying gender through facial images is a crucial aspect in various life contexts. Biometric technology, such as facial recognition, has become an integral part of various applications, including fraud detection, cybersecurity protection, and consumer behavior analysis.  With the advancement of technology and the progress in artificial intelligence, especially through the use of Convolutional Neural Networks (CNNs), computers can now identify gender from facial images with a high level of accuracy. Although there are still some challenges, such as variations in pose, facial expressions, and different lighting conditions, CNNs can overcome these obstacles. This study uses the CelebA dataset, which consists of 122,000 facial images of both men and women. The dataset has been processed to maintain a balanced number of samples for each gender class, resulting in a total of 101,568 samples. The data is divided into training, validation, and test sets, with 80% used for training, and the remaining 20% split between validation and testing. Eight different CNN architectures are applied, including VGG16, VGG19, MobileNetV2, ResNet-50, ResNet-50 V2, Inception V3, Inception ResNet V2, and AlexNet. Although previous research has shown the potential of CNN architectures for various classification tasks, these studies often encounter issues of overfitting on large datasets, which can reduce model accuracy. This study applies dropout techniques and hyperparameter tuning to address overfitting issues and optimize model performance. The training results indicate that ResNet-50, ResNet-50 V2, and Inception V3 achieved the highest accuracy of 98%, while VGG16, VGG19, MobileNetV2, and AlexNet achieved accuracies of 95% and 97%, respectively. Performance evaluation using confusion matrices, precision, recall, and F1-score demonstrates excellent performance.
DEVELOPMENT OF INFORMATION SYSTEM FOR EMPLOYEE PERFORMANCE ASSESSMENT AT HASNUR CENTRE USING 360° ASSESSMENT Arif Muhammad Iqbal; Angelin Cahyaning; Shofiana Primi Rusdiana; Brina Miftahurrohmah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.5180

Abstract

Hasnur Centre is the CSR institution of Hasnur Group dedicated to the development of human resources in South Kalimantan. The performance assessment for Hasnur Centre employees currently relies on a conventional and unidirectional brief fill-in-the-blank method, reflecting the viewpoint of superior, indicating a need for an adjustment in the employed method. Furthermore, the employee evaluation process at Hasnur Centre still relies on a simple Google Form. Therefore, there is a need for the development of information system integration that can automate employee performance assessment at Hasnur Centre. The system is developed gradually according to the needs of the HR Admin, utilizing the Spiral development method. The tools include Use Case Diagrams, PHP as the programming language, CodeIgniter as the system development framework, and MySQL. This research has resulted in the Employee Performance Assessment Information System for Hasnur Centre employees, introducing a novelty by integrating the 360° Assessment method based on predetermined perspectives and sub-perspectives using a Likert Scale combined with a brief qualitative input method in which evaluators provide written feedback on the assessed employees. The combination of these two methods results in a more measurable, objective, and unbiased performance evaluation, making it a reliable tool for the Executive Director of Hasnur Centre in making decisions related to employee performance
MEASURING PERCEIVED USABILITY OF ARTIFICIAL INTELLIGENCE-BASED QUIZZES IN A VIRTUAL MUSEUM Shinta Puspasari; Rendra Gustriansyah; Dwi Asa Verano; Ahmad Sanmorino; Hartini Hartini; Ermatita Ermatita
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.5611

Abstract

The transformation of modern museums through digital technology offers added value to visitors, especially in the context of education. Virtual museums, in particular, complement physical museums by providing accessibility and enhancing the learning experience. The SMBII virtual museum includes an AI-based quizzes feature designed to assess the knowledge level of visitors regarding the museum's history and collections as an educational feature. In addition to physical museums, virtual museums offer convenience and enrich the learning process for visitors. The quizzes adapts its questions based on the visitor's profile, leveraging AI to tailor content and maximize learning outcomes. This study aims to compare the effectiveness of two widely used usability metrics—System Usability Scale (SUS) and Usability Metric for User Experience (UMUX)—in evaluating the usability of the AI-driven quiz feature within the SMBII virtual museum. The study specifically seeks to determine whether there are significant differences between SUS and UMUX in measuring user perceptions of the quiz’s usability. The primary respondents of this study were students, who represent the museum's target audience for educational purposes. Hypothesis testing results show no significant difference between the SUS and UMUX scores (P > 0.05), indicating that both metrics offer similar evaluations of usability. Based on these findings, the study recommends the use of UMUX over SUS for future usability assessments in virtual museum systems, as UMUX is more time-efficient without compromising accuracy. This research contributes to advancing the understanding of usability testing methods for AI-based educational features in virtual museum environments

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