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All Journal Jurnal Sains dan Teknologi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pilar Nusa Mandiri Jurnal Ekobistek Sebatik MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Pendidikan Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education Jurnal Komtekinfo SATIN - Sains dan Teknologi Informasi Journal Of Informatics And Busisnes Jurnal Teknik dan teknologi tepat guna The Indonesian Journal of Computer Science Jurnal TAM (Technology Acceptance Model)
Syafri Arlis
Universitas Putra Indonesia YPTK
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Decision Support System for Selecting Casual Daily Workers to Become Permanent Employees Using the Profile Matching Method Edwar, Eggy Febyanti; Yuhandri; Arlis, Syafri
Journal of Computer Scine and Information Technology Volume 10 Issue 4 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i4.109

Abstract

Information is the result of processing data from one or more sources, which is then processed to provide value, meaning and benefits. In modern times, the use of technology plays a very important role as a means of information and promotion, especially in the field of websites in delivering information. Technological advances in the field of computers are very helpful in the current decision-making process. One method of decision support systems is profile matching. This method is used to determine the assessment in selecting daily employees to become employees. Profile matching is broadly a process of comparing individual competition in job competition so that the difference in competition (also called gap) can be known, the smaller the gap produced, the greater the weight of the value which means that there is a greater chance for employees to occupy the position. After the calculation using the Profile Matching method, the ranking value that meets the requirements is in the alternative with the name of the worker, namely Bakhtiar with a score of 4.535 and is recommended to become a permanent employee. By applying this method, it is very helpful in determining the selection of casual laborers to become permanent employees.
Penerapan Deep Learning Menggunakan Metode Convolutional Neural Network dan K-Means dalam Klasterisasi Citra Butiran Pasir Olivia, Ladyka Febby; Yuhandri, Y; Arlis, Syafri
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.v12i1.629

Abstract

Agregat halus (pasir) merupakan bahan bangunan yang paling banyak digunakan dalam dunia konstruksi, sehingga kebutuhan pasir setiap harinya sangat banyak terutama di daerah perkotaan yang pembangunannya sangat pesat. Pasir berbentuk butiran - butiran yang memiliki tekstur berbeda untuk setiap jenisnya. Karakteristik pasir yang baik apat ditentukan melalui beberapa parameter, seperti segi kadar lumpur pasir, pemeriksaan kadar air nyata dan SSD, pemeriksaan gradasi, kadar air, zat organik, berat isi kondisi padat/gembur, daya serap, modulus kehalusan. faktor-faktor ini menjadi acuan dalam memilih pasir yang sesuai untuk berbagai kebutuhan konstruksi, termasuk plesteran dinding dan lantai. Parameter-parameter ini menjadi acuan dalam memilih pasir yang tepat untuk digunakan dalam berbagai kebutuhan konstruksi, termasuk plesteran dinding dan lantai. penelitian ini bertujuan untuk mengelompokkan kesesuaian antara butiran pasir untuk plesteran dinding atau lantai. Gambar dari citra butiran pasir memiliki nilai piksel yang banyak kerena terdiri dari tiga komponen warna yang mana red, green, blue. Sehingga membutuhkan teknik yang baik dalam menganalisa gambar ini. Metode yang digunakan dalam penelitian ini adalah Convulutional neural network (CNN) sebagai untuk mendeteksi dan mengekstraksi fitur butiran pasir, Convolutional Neural Network yang digunakan dalam penelitian ini adalah arsitektur resNet 50 sebagai memiliki kinerja tinggi dalam analisis citra.. Convolutional Neural Network memiliki arsitektur yang terinspirasi oleh struktur visual sistem manusia dan sangat efektif untuk tugas-tugas dalam ekstraksi gambar dan Metode K-means Clustering untuk menentukan pengelompokkan data ke dalam beberapa kelompok (klaster) sehingga data dalam satu klaster memiliki kemiripan tinggi sementara data antar klaster berbeda secara signifikan butiran pasir. Dataset yang diolah dalam penelitian ini bersumber di CV. Sumber Rezeki. Dataset terdiri 94 citra butiran pasir. Hasil penelitian menunjukkan bahwa pasir dapat diklasifikasikan ke dalam beberapa kategori mengelompokan seperti butiran bulat, butiran tajam, butiran tumpul, butiran tidak beraturan, butiran sub angular. Penelitian ini dapat menjadi acuan dalam menentukan kesesuaian butiran citra pasir yang cocok untuk lantai atau plesteran dinding dan membantu kontraktor memilih jenis pasir.
Customer Relationship Management Increasing Curtain Sales with Importance Performance Analysis and Customer Satisfaction Index Methods Andrian, Reski; Islami, Fajrul; Arlis, Syafri
Journal of Computer Scine and Information Technology Volume 11 Issue 1 (2025): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v11i1.132

Abstract

The increasingly rapid advancement of technology has an indirect impact on humans. Computers are one of the results of technological advances that can help humans improve the quality and quantity of work. By using computers as one of the tools in presenting data information, especially data processing supported by PHP and MySQL applications, it can support the speed of data processing as efficiently as possible. This study uses the Importance Performance Analysis (IPA) Method and the Customer Satisfaction Index (CSI) Method. The IPA method is a method used to map the relationship between interests and performance of each attribute to meet consumer satisfaction by providing a Likert scale. While the CSI method is a method used to determine the level of consumer satisfaction as a whole by looking at the level of satisfaction of product and service attributes. Based on the combination of the IPA and CSI methods carried out directly in the field using interview techniques, as well as by studying books related to the problems discussed, it is expected that the new system that will be implemented can improve the quality of information so that it can be useful for the relevant agencies. The results of this study are based on the consumer satisfaction index of 82.24% , so that by applying these two methods, it can find consumer satisfaction in increasing curtain sales.
Machine Learning Analisis Klasifikasi dalam Penentuan Status Gizi Anak Yanto, Musli; Febri Hadi; Syafri Arlis
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5278

Abstract

Malnutrition is one of the problems that occurs in children due to a lack of nutritional intake. Indonesia contributed 36%, making it the fifth country with the largest cases of malnutrition in the world. On this basis, a solution is needed to reduce the growth rate of malnutrition cases. This research aims to carry out classification analysis to determine nutritional status by optimizing machine learning (ML) performance. The ML classification analysis process will later utilize the performance of the artificial neural network (ANN) method with the Multilayer Perceptron (MLP) algorithm. ML performance can be optimized using the Pearson’s correlation (PC) method to produce optimal classification analysis patterns. This research data set uses child nutrition case data from 576 patients sourced from the M. Djamil Padang Province Regional General Hospital (RSUP). The data set is divided into 417 training data and 159 test data. On the basis of the tests that have been carried out, the performance of the PC method can provide precise and accurate analysis patterns. This analysis pattern has also been able to provide a fairly good level of accuracy, namely 95%. Not only that, this research is also able to present analysis patterns with the best ANN architectural model in classifying nutritional status. Based on the overall results, this research can be used as an alternative solution to the treatment of nutritional problems in children.
ON LINE TICKET SALES RESERVATION SYSTEM AT PO. SRIWIJAYA BENGKULU Supperianto, Bambang; Hendrik, Billy; Arlis, Syafri
Jurnal TAM (Technology Acceptance Model) Vol 16, No 1 (2025): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v16i1.1795

Abstract

The development of science and technology along with the times can support all activities carried out by humans. A computer is a set of electronic devices that are largely composed of hardware and software that work together in processing data provided or entered by the user. Computers have many features, namely being able to process data that are numbers, letters, images, sounds, symbols, and others. With computer technology, data processing will be easier and more efficient so that users do not need a long time to process data. PO. Sriwijaya Bengkulu is one of the service companies engaged in transportation. However, the processing of sales and purchase administration data at PO. Sriwijaya Bengkulu in booking tickets for this company is still using a manual system which makes it difficult for consumers to be able to order tickets so that consumers have to come to PO. Sriwijaya Bengkulu directly to do the ticket booking process. This research aims to create a new booking system that uses PHP software and MySQL database connected to internet technology at PO. Sriwijaya Bengkulu, so that booking tickets is easier and wider. The result of this research is an online sales system that is often called a reservation that can be accessed easily and efficiently.
PADANG FOOD IMAGE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN) Permana, Nabilah Putri; Arlis, Syafri
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.7388

Abstract

The recognition of Padang traditional foods presents a challenge because of their high visual similarity, which makes manual classification difficult. This study aims to develop an automatic image classification model for Padang foods using the Convolutional Neural Network (CNN) algorithm. The dataset consisted of 1350 images across nine classes of Padang dishes including omelet, chili egg, cow tendon curry, stuffed intestine curry, fish curry, dendeng batokok, rendang, ayam pop, and fried chicken. The CNN architecture was trained for twenty epochs and evaluated using accuracy, loss, confusion matrix, and testing with new images. The results show that the model reached a final training accuracy of 70.2 percent and a validation accuracy of 65 percent, while testing with unseen images produced correct predictions with moderate confidence levels. These findings suggest that CNN is effective for classifying Padang traditional foods and can be applied in culinary promotion, digital food catalogs, and technology based ordering platforms.
Optimization of Data Envelopment Analysis Method with MOORA in the Selection of Research Proposals and PKM Ridwan, Ridwan; Arlis, Syafri; Sumijan
Jurnal KomtekInfo Vol. 12 No. 3 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i3.652

Abstract

The quality of the selection of research proposals and Community Service (PKM) of lecturers is an important element in supporting the implementation of the Tridarma of Higher Education. However, the selection process that is still carried out manually and tends to be subjective has the potential to cause bias in decision-making. This research aims to develop a decision support system that integrates the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) and Data Envelopment Analysis (DEA) methods to increase objectivity and efficiency in the selection process of research proposals and PKM lecturers at Rokania University. The MOORA method is used to determine a preference score based on the five main criteria for research proposals and six main criteria for PKM proposals, while the DEA method is utilized to evaluate the relative efficiency of each proposal based on the ratio between the MOORA score and the amount of funding submitted. The data used in this study was obtained from the results of the assessment of three reviewers on 14 research proposals and 11 PKM proposals. Each proposal is assessed based on criteria that have been determined by LPPM, then calculations are carried out using both methods. The results show that the combination of MOORA and DEA methods is able to produce more transparent and fair proposal rankings, as well as being able to identify the most efficient proposals in the use of the budget. This study concludes that the integration of MOORA and DEA methods in the lecturer proposal selection system is able to strengthen data-based research and PKM governance, as well as make a real contribution to more rational and measurable decision-making. This system also has the potential to be further developed to support the selection of external grants, recruitment of reviewers, or the allocation of research funds nationally. These findings can be replicated in other higher education institutions that face similar challenges.
Shortest Path Navigation Optimization using Algorithm A* in GPS-Based Augmented Reality Visualization Aswandi, Nopan; Hendrik, Billy; Arlis, Syafri
Jurnal KomtekInfo Vol. 12 No. 3 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i3.654

Abstract

The development of digital technology in the Society 5.0 era encourages the integration of technology-based solutions in various fields, including higher education. Ibnu Sina University faces challenges in providing informative and accurate access to campus navigation, especially for new students and visitors. The lack of a directional system and the absence of a digital map of the campus cause difficulties in finding service locations such as the rectorate, academic room, or finance department. Conventional technologies such as Google Maps have not been able to provide specific navigation in the campus environment due to the limitations of internal building mapping. This research aims to develop a campus navigation system based on Augmented Reality (AR) and Global Positioning System (GPS) that is optimized with the A* (A-Star) algorithm for the determination of the shortest path. AR technology is used to display visual directions in real-time in a real environment through a smartphone camera. GPS plays a role in determining the user's position, while the A* algorithm calculates the shortest route based on the structure of the campus location graph using a heuristic approach. The development method used is the Multimedia Development Life Cycle (MDLC) which consists of six stages: concept, design, material collecting, assembly, testing, and distribution. Data collection was carried out through direct observation, interviews with the campus, and recording 14 coordinate points of campus service locations using Google Earth. The system is built using the Unity platform with support from the AR Foundation and ARLocation, as well as the A* implementation in route search. The results of this research are able to provide a more efficient, accurate, and interactive campus navigation solution. This system not only supports the convenience of users in finding a location, but also becomes an innovation in the development of campus services based on digital technology.
Large Language Model Method as a Translator Indonesian Into SQL Language Putra, Candra; Arlis, Syafri; Nurcahyo, Gunadi Widi
Jurnal KomtekInfo Vol. 12 No. 3 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i3.658

Abstract

The development of information technology has encouraged the massive implementation of information systems and web-based applications in various sectors, including in the academic environment. However, one of the challenges that are still often faced is the difficulty in extracting or mining information from databases flexibly without having to create additional report modules or write SQL code manually. This problem becomes an obstacle for non-technical users, such as administrative staff or lecturers, who need certain data quickly from academic information systems. In this paper, it is intended to convert Indonesian commands into SQL queries automatically, without the need to add additional programming code. Along with advances in Natural Language Processing (NLP) and Machine Learning technology with the Large Language Model (LLM) method, there is now a new approach that allows users to interact with databases only through commands in natural language. The case study was conducted on the Academic Information System of UIN Padangsidimpuan using a dataset of 1,500 student data. The focus of the research is on the type of Data Query Language (DQL) query in Indonesian form, which is then translated by the model into a SQL command to obtain the desired data. The results showed that this approach was able to achieve results with a Rouge1 conversion precision rate from 0.03 to 0.89. This shows that the integration of LLM technology in academic information systems has great potential in improving data accessibility, operational efficiency, and supporting data-driven decision-making faster and more intuitively, especially for users who do not have a technical background.
Application of Forward Chaining and Certainty Factor Methods to Identify Anxiety Disorder Categories Doli Raharjo, Tio; Widi Nurcahyo, Gunadi; Arlis, Syafri
Jurnal KomtekInfo Vol. 12 No. 3 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i3.663

Abstract

Anxiety disorders are a form of mental disorders that often occur and have a significant impact on the quality of life of individuals. However, the process of diagnosing this disorder still faces various challenges, especially limited access to professionals and difficulties in identifying the type of disorder based on varying symptoms. This research aims to design and implement an expert-based system to help the early diagnosis process of anxiety disorders quickly and accurately. The system was developed as a web application that allows users to answer a series of questions related to the symptoms experienced, then provide possible types of disorders based on the calculation of confidence levels. The method used is forward chaining as an inference engine to conduct a rule and certainty factor search to calculate the level of confidence in the identification results of the symptoms experienced by the user. Data collected from the literature and interviews with experts were built into a knowledge base consisting of 8 types of anxiety disorders with a total of 41 symptoms. Each rule in the system is formulated using an if-then structure that combines CF values to represent the level of confidence in the symptoms and the results of logical inference with advanced tracking methods. The system was tested using 20 test data in the form of symptom-based case simulations. The results of the evaluation showed that the system was able to produce an initial diagnosis with an accuracy rate of up to 100% based on comparison with manual diagnosis from experts. This system also provides explanatory information in the form of confidence level in each diagnosis result. These findings suggest that the Certainty Factor and Forward Chaining approaches are effective in building expert systems for diagnosing anxiety disorders and have the potential to be further developed as a screening tool in educational or primary health care settings.
Co-Authors Agung Ramadhanu Andrian, Reski Aswandi, Nopan Candra Putra Darma Syahrullah Ekajaya Dodi Guswandi Doli Raharjo, Tio Edwar, Eggy Febyanti Fajrul Islami Febri Hadi Feri Pratama Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra Guslendra Hanum, Luthfia Harkamsyah Andrianof Hendrik, Billy Johan Harlan Jufriadif Na`am, Jufriadif Larissa Navia Rani Larissa Naviarani M.Iqbal, M.Iqbal Mardison Mardison Marfalino, Hari Muhammad, Abulwafa Musli Yanto Nabila Frisca Oktavia Nur, Rofil M Nurcahyo, Yuhandri Olivia, Ladyka Febby Permana, Nabilah Putri Prayuda, Muhammad Hozi Putra, Deri Marse Putra, Muhammad Reza Ridwan, Ridwan Rini Sovia Rofil M. Nur Sahari Sahari Sahari Sahari Sahari Sarjon Defit Sarjon Defit SIGIT SANJAYA, SIGIT Sofika Enggari Sumijan Sumijan Sumijan Sumijan Supperianto, Bambang Syahputra, Afriadi Wahyu, Fungki Winda Sutra Amanda Yanto, Musli Yuhandri Yuhandri Yunus Yuhandri, Y Yulasmi