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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Komtekinfo Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Sumijan, S
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Technology Readiness Index untuk Menganalisis Kesiapan Adopsi Teknologi Kecerdasan Buatan Mahasiswa Komputer Wirahmadayanti, Isna; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.584

Abstract

The education sector combined with the branch of artificial intelligence has great potential to change the way information is accessed and managed to improve the learning experience and support decision making in the educational process. It is important to understand the level of readiness for the adoption of artificial intelligence among students as the main stakeholders in the educational environment. The purpose of this study was to determine the readiness for adoption of technology, and what factors influence the readiness for adoption of artificial intelligence in Computer Science Students at Universitas Putra Indonesia "YPTK" Padang. This study uses the Technology Readiness Index (TRI) method which consists of four variables, including the variables of optimism, innovativeness, discomfort, and insecurity. The Technology Readiness Index (TRI) measures a person's tendency to accept and use technology to complete goals in their home life or at work. This study was conducted by distributing questionnaires to 348 students consisting of students of information systems and informatics engineering study programs. Data were obtained from a total population of 2689 students, 348 samples were obtained based on the Slovin formula with an error margin of 5%. Determination of the sample to determine the number of samples of each stratum in the population with proportionate stratified random sampling in the Information Systems study program of as many as 250 students and the Informatics Engineering study program of 98 students. Manual calculations and using applications show that computer students at Universitas Putra Indonesia “YPTK” Padang are very ready to adopt artificial intelligence technology with variable values ​​of optimism 93.27%, innovative 92.64%, discomfort 91.66%, and insecurity 88.73%. These results can be stated that the factors that influence the readiness to adopt artificial intelligence technology include optimism, innovative, discomfort, and insecurity with a median index value of all variables of 92.15%
Prediksi Jumlah Kunjungan Pasien pada Bidan Praktik Mandiri dengan Jaringan Syaraf Tiruan Backpropagation Rifky, Muhammad; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.628

Abstract

Independent Midwives (BPM) are important in providing health services for mothers and children. One of the main challenges in managing BPM is the uncertain fluctuation in patient visits, making it difficult to plan resources, such as medical personnel, drug supplies, and other supporting facilities. If the number of patient visits cannot be predicted properly, the risk of shortages or excess resources becomes higher, which can impact operational efficiency and the quality of health services. Uncertainty in the number of patients can also affect financial planning and readiness to face a surge in visits. Based on this, this study aims to develop a prediction model for the number of patient visits using Artificial Neural Networks (ANN) with the Backpropagation method. The dataset uses data on the number of Antenatal Care (ANC) patient visits over the past three years. The results of the model evaluation were carried out based on the Mean Squared Error (MSE) value and the prediction accuracy level presented more than 94% accuracy level. The evaluation results also obtained an MSE value of 0.0023, and MAPE of 5.62% so that the results can be stated that the model prediction error is within acceptable limits. This predictive model can contribute to assisting BPM in resource planning, improving service efficiency, and strategic decision-making in managing health facilities
Penerapan Artificial Neural Network untuk Memprediksi Persediaan Obat Esensial Alfallah, Fadhly; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 1 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i1.630

Abstract

The availability of essential medicines is a fundamental factor in ensuring high-quality healthcare services, especially in primary healthcare facilities such as Puskesmas. Inefficient drug inventory management can lead to various issues, including drug shortages that disrupt medical services and overstocking that may result in waste due to expiration. An accurate prediction system is essential to support more effective and efficient drug inventory planning. This study aims to analyze historical drug usage patterns to generate more accurate predictions. The research methodology includes problem identification, data collection, preprocessing, ANN architecture design, implementation, and system evaluation. Historical drug usage data from previous years is used for training and testing, with a division of 70% for training and 30% for testing. The backpropagation algorithm is applied to optimize the model by adjusting parameters such as the number of neurons in the hidden layer, learning rate, and activation function. The study results show that the ANN model with a 12-12-1 architecture achieves a high prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of 2.13% for paracetamol stock. The developed MATLAB application provides an interactive platform for users to input historical data and obtain dynamic stock predictions. This system implementation is expected to help Puskesmas manage drug inventory more effectively, reduce the risks of shortages and overstocking, and improve efficiency in essential drug distribution. This study contributes to the field of health informatics by demonstrating the effectiveness of ANN in drug inventory prediction. Future research may explore hybrid machine learning models or integrate external factors, such as seasonal disease patterns and community demand levels, to enhance predictive accuracy and adaptability.
Evaluation of E-Government Governance with the Implementation of the COBIT 2019 Method at the West Pasaman Civil Registration Office Septiawan, Edo; Veri, Jhon; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.651

Abstract

The rapid development of information technology has encouraged government agencies to adopt Electronic Government (E-Government) to improve the efficiency, transparency, and accountability of public services. However, implementation challenges remain, especially in regional agencies such as the Population and Civil Registration Office (Dukcapil) of West Pasaman Regency, where inconsistencies and lack of system integration are still widespread. This study aims to analyze and evaluate E-Government governance. The method used in this study is the COBIT 2019 framework to assess the level of capability and propose strategic improvements in IT service management. This study adopted a qualitative case study approach, guided by the COBIT 2019 Design and Implementation framework. Data were collected through direct observation and in-depth interviews with Dukcapil personnel, supported by 145 structured questions and documentary analysis. This process focuses on five COBIT processes: EDM04 (Ensure Resource Optimization), APO07 (Manage Human Resources), BAI09 (Manage Assets), DSS01 (Manage Operations), and MEA01 (Monitor, Evaluate, and Assess Performance and Conformance). The results show that the capability level of the DSS01 process is at level 2 (Managed Process), while the capability target is level 3 (Established Process). This gap reflects the need for improvement in operational process management, including strengthening documentation and standardizing practices. Based on these results, strategic recommendations are developed that are contextually oriented to local bureaucratic conditions. This research can be a reference for the implementation of COBIT 2019 to effectively identify governance weaknesses and provide actionable strategies in increasing E-Government maturity at the regional level, supporting better service delivery and organizational performance.
Convolutional Neural Network Method in Detecting Digital Image Based Physical Violence Elpina, Elpina Sari Dewi Hasibuan; Yuhandri, Y; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.657

Abstract

Physical violence in the educational environment has a serious impact on mental health, safety, and student achievement, in addition to causing physical injury, violence can cause psychological trauma that interferes with the learning process, due to the limited supervision system, lack of officers, and the absence of automatic detection technology. This research aims to design and develop an automatic detection system of physical violence using digital image processing technology. This study uses the Convolutional Neural Network (CNN) method with the stages of digital image collection and labeling, preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The CNN architecture was chosen because it is efficient and accurate, and it supports data augmentation to improve generalization. The dataset was taken from kaggle and primary data at the al-falah huraba Islamic boarding school which consisted of 2000 images which included: 800 images of violence on CCTV of the dormitory room, 500 images of violence simulation of training videos and 500 non-violent images. The results showed that the developed CNN model was able to detect physical violence with an accuracy of above 88%, making it feasible to apply in surveillance camera-based school surveillance systems (CCTV). The system is able to classify images in real-time into two categories: safe and hard. This research contributes to the use of artificial intelligence to support efficient and affordable technology-based education security.
Decision Support System in Determining TPQ/TQA Teacher Certification Categories Using the SAW Method Zikri, Afdal; Nurcahyo, Gunadi Widi; Sumijan, S
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.666

Abstract

TPQ/TQA teacher certification is an effort to improve the quality of educators in deepening their knowledge of the Qur'an. The certification assessment process often faces challenges related to subjectivity and inconsistencies in criteria, thus requiring a decision support system capable of producing more objective and measurable assessment results. Based on the problems described above, this study aims to analyze the TPQ/TQA teacher certification assessment in Padang City. The SAW method is very suitable for this study because of its ability to perform calculations based on predetermined criteria. The research data consists of 60 assessment documents. The analysis process includes determining criteria, normalizing weights, calculations, and rankings. Based on the 60 datasets, 9 individuals obtained a certification score of A, 11 obtained a B, and 40 obtained a C. The results of this study indicate that the decision support system is capable of providing highly accurate, transparent, and efficient results in determining TPQ/TQA teacher certification scores. These findings are expected to be useful for TPQ/TQA management institutions in determining certification scores.
Co-Authors Ade Silvia, Ade ahmad yani Alfallah, Fadhly Alfarisdon, A Ayudia, Dina Damanik, Abdi Rahim Elda, Yusma Elpina, Elpina Sari Dewi Hasibuan Gunadi Widi Nurcahyo Hari, Tio Ramadan Sapto Karim, Fajri Khairani, Maisan Dewi Puspa Lubis, Mustopa Husein M. Syahputra Okta Andrica Putra Ramadhanu, Agung Rifky, Muhammad Salmiati, S Sari, Imrah Sarjon Defit Septiawan, Edo Setiawan, Adil Syafrika Deni Rizki, Syafrika Deni Syaljumairi, Raemon Tamin, Zulfiqar Veri, Jhon Wardana, Bendra Wirahmadayanti, Isna Yemi, Leonardo Yuhandri Yuhandri, Yuhandri Yuhandri, Y Zikri, Afdal Zulvitri, Z