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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) bit-Tech Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Informasi dan Teknologi Jurnal Perangkat Lunak Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer Scine and Information Technology Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Informatika Ekonomi Bisnis RJOCS (Riau Journal of Computer Science) JR : Jurnal Responsive Teknik Informatika The Indonesian Journal of Computer Science Jurnal Responsive Teknik Informatika
Sumijan
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Agriculture, Biological Sciences & Forestry Arts Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics

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ALGORITMA ASSOCIATION RULE METODE FP-GROWTH MENGANALISA TINGKAT KEJAHATAN PENCURIAN MOTOR (STUDI KASUS DI POLRESTA PADANG) Suri, Ghea Paulina; Defit, Sarjon; Sumijan
Jurnal Responsive Teknik Informatika Vol. 2 No. 01 (2018): JR : Jurnal Responsive Teknik Informatika
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36352/jr.v2i01.222

Abstract

Kendaraan bermotor merupakan sarana vital dengan mobilitas tinggi yang sangat diperlukan untuk kehidupan di era modern ini. Salah satu cara yang dapat dilakukan untuk penentuan strategi tersebut adalah dengan menggunakan teknik data mining. Adapun teknik yang digunakan Algoritma FP-Growth adalah salah satu alternatif algoritma yang dapat digunakan untuk menentukan himpunan data yang paling sering muncul (frequent itemset) dalam sekumpulan data. Tujuan dari penelitian ini adalah membangun suatu pengetahuan baru dalam menganalisa tingkat kasus pencurian motor dan memberikan informasi kepada kepolisian dalam mengatasi tingkat kejahatan. Sumber data masih belum lengkap karna data mentahnya masih belum diolah, data yang diambil merupakan data pencurian motor yang mencakup laporan dipolresta padang. Data yang di dapat memiliki atribut pekerjaan dan terlapor, data yang telah didapat belum bisa langsung diolah dan dikumpulkan dan diberi kode agar mudah dalam pemrosesan atau pengolahan data mining. Hasil dari pengujian terhadap metode ini maka didapatkan informasi untuk dapat membantu kepolisian dalam mengatasi tingkat kejahatan pada pencurian sepeda motor dan mengimplementasikan algoritma FP-Growth yang menggunakan konsep pembangunan FP-Tree dalam mencari Frequent Itemset. Maka dihasilkan Association Rule.
Analisis Prediksi Penjualan Suku Cadang Motor dengan Metode Monte Carlo Rais, Edo Rinaldi; Sovia, Rini; Sumijan
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2231

Abstract

Peramalan penjualan merupakan salah satu aspek penting dalam strategi manajemen bisnis, terutama dalam industri otomotif yang memiliki pola permintaan yang fluktuatif. Manajemen stok yang tidak optimal dapat menyebabkan overstock atau stockout, yang berdampak pada efisiensi operasional dan kepuasan pelanggan. Penelitian ini bertujuan untuk menerapkan metode Monte Carlo dalam memprediksi penjualan suku cadang motor di Bengkel Ilham Motor, guna meningkatkan akurasi prediksi dan membantu optimalisasi pengelolaan persediaan barang. Metode penelitian ini menggunakan data historis penjualan tahun 2024, yang dianalisis melalui beberapa tahapan: penentuan distribusi probabilitas, pembangkitan angka acak, simulasi Monte Carlo, dan validasi hasil prediksi. Implementasi metode ini dikembangkan dalam sistem berbasis web, menggunakan PHP sebagai bahasa pemrograman dan MySQL sebagai basis data. Hasil penelitian menunjukkan bahwa metode Monte Carlo mampu memberikan tingkat akurasi prediksi yang tinggi, dengan rincian sebagai berikut: oli (95,33%), kampas rem (99,59%), lampu depan (97,27%), saringan udara (97,53%), busi (95,78%), dan sil karet (97,32%). Prediksi yang dihasilkan memungkinkan bengkel untuk menentukan jumlah stok yang lebih optimal, sehingga dapat menghindari kelebihan maupun kekurangan persediaan. Selain itu, sistem berbasis web yang dikembangkan terbukti dapat mempercepat analisis data dan membantu dalam pengambilan keputusan bisnis yang lebih akurat. Kesimpulan dari penelitian ini adalah bahwa metode Monte Carlo dapat diandalkan sebagai pendekatan prediktif dalam perencanaan stok suku cadang motor. Untuk pengembangan lebih lanjut, disarankan agar model ini dikombinasikan dengan teknik machine learning atau mempertimbangkan faktor eksternal seperti tren pasar dan harga bahan baku guna meningkatkan akurasi prediksi.
PENERAPAN MACHINE LEARNING MENGGUNAKAN ALGORITMA DECISION TREE UNTUK PREDIKSI TINGKAT KELULUSAN MAHASISWA Nst, Ely Nurhalizah; Sumijan; Nurcahyo, Gunadi Widi
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/ctjbgh21

Abstract

Students are an integral part of higher education institutions, where graduation rates serve as a key indicator of academic quality and institutional effectiveness. To maintain accreditation and academic standards, universities must optimize student graduation rates. Evaluating the factors influencing graduation is crucial in identifying patterns and key determinants that contribute to academic success. This study aims to predict student graduation using Machine Learning, specifically the C5.0 Decision Tree algorithm. The findings indicate a high reliability in predicting student graduation, with an accuracy of 91.35%. The model's ability to identify on-time graduates is reflected in a recall of 93.85% for the On-Time category and 87.18% for the Delayed category. The prediction accuracy is further demonstrated by a precision of 92.42% for the On-Time category and 89.47% for the Delayed category. The F1-Score, which represents the balance between recall and precision, reaches 93.12% for the On-Time category and 88.32% for the Delayed category. These evaluation metrics indicate that the C5.0 algorithm effectively classifies students based on their likelihood of graduating with high accuracy. The predictions generated can serve as a reference for universities to identify at-risk students early, allowing the implementation of appropriate academic strategies to improve graduation rates, accreditation, and institutional quality.
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.
Utilization of Convolutional Neural Network Method in Customer Identification Based on Facial Images Ade, Ade Puspita Sari; Sarjon Defit; 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.v12i3.664

Abstract

Artificial intelligence-based facial recognition technology, especially using the Convolutional Neural Network (CNN) method, is increasingly widespread in various business applications, such as customer data management. This technology allows the system to recognize and identify individuals automatically through facial images, so it is very potential to be applied in customer management. This study aims to implement CNN technology in automatically identifying old customers in a case study in JAVApace Studio. CNN method for facial recognition, optimizing the accuracy of old customer identification, designing CNN system integration in computer vision-based applications, and measuring CNN performance in real-time facial identification. The research method was carried out using a quantitative approach through data collection stages in the form of 875 customer facial images taken in JAVapace Studio, data preprocessing (cropping, resizing, and data augmentation), dataset division for training, validation, and testing. The CNN model used is the ResNet-50 architecture with fine-tuning techniques and freezing layers to improve training efficiency. Model performance evaluation uses a confusion matrix with accuracy, recall, and precision metrics. The results show that the CNN-based facial recognition system achieved 95.7% accuracy in distinguishing existing customers from the test data used. The recall rate was 94.5%, while the precision rate reached 96.2%. The discussion of the results also indicates that the fine-tuning approach is effective in optimizing model performance with an inference time suitable for real-time implementation needs. This study confirms that the implementation of CNN with ResNet-50 architecture is effectively able to recognize the faces of old customers with high levels of accuracy, recall, and precision, making it the right solution in managing customer data automatically and efficiently.
Expert System for Diagnosing Malnutrition Using the Certainty Factor Method Hakim, Wijaya; Sumijan; Akhiyar, Dinul
Journal of Computer Scine and Information Technology Volume 10 Issue 1 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i1.95

Abstract

Malnutrition in toddlers causes a negative impact on motor nerve development, inhibits behavioral and cognitive development causing a decrease in academic performance and social skills . In addition, malnutrition during infancy can cause long-term risks that focus on later in life, increasing the risk of disease or disability or even death. With advances in information technology today, it is very helpful in predicting or identifying an event, one of which is an expert system that can help an expert in identifying a disease in the world of medicine. Therefore, an expert system is needed that can help doctors and the public find out the type of malnutrition they are suffering from based on the symptoms they are experiencing. The expert system uses the Certainty Factor method in reasoning to obtain diagnostic results from the symptoms shown. This method uses the value of an expert's belief in the symptoms of a disease. The aim of this research is to apply the certainty factor method in identifying malnutrition and providing definitions and suggestions for the disease suffered. The expert system was built using PHP and MySQL database. The results of applying the Certainty Factor method based on the tested data showed that the disease suffered by the patient was Kwarshiorkor with a Certainty Factor level of 0.958528 or 95%. The results of this test show that the certainty factor method expert system is able to identify a disease based on the symptoms experienced
Implementation of the Topsis and AHP Methods in the Decision Support System for Determining the Best Employees Putri, Yolan Ananda; Sumijan; Enggari, Sofika
Journal of Computer Scine and Information Technology Volume 10 Issue 2 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i2.103

Abstract

Every company or agency needs Human Resources (HR) in the form of employees who have competence and good performance. Employees are one of the most important assets owned by a company. The West Sumatra Province Transportation Service is the organizer of government affairs in the field of transportation or transportation policy for the West Sumatra Province region where the selection of the best employees is still not optimal using Microsoft Excel. The aim of designing a new system at the Provincial Transportation Service is to create optimization in the assessment of each employee to facilitate the recapitulation of employee data. The data is analyzed and processed according to the research framework, namely using a Decision Support System, especially the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP) methods. In this research, 10 alternative employees were taken to be assessed. Based on formula calculations using the AHP method, it is used to determine the weighted value of each existing criterion, then the resulting values from the weighting are used to carry out rankings using the TOPSIS method. After carrying out calculations using these 2 methods, the result was that the best employee was alternative 9 in the name of Rusdi with a value of 0.9995. So with this calculation the results can show which employees have the right to be the best employees in that agency
Application of Fuzzy Logic to Classify Community Welfare Levels Aditra; Sumijan; Sovia, Rini
Journal of Computer Scine and Information Technology Volume 10 Issue 3 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i3.104

Abstract

Information regarding family welfare does not only affect family members, but also influences the success of government, including village government. Therefore, information regarding the level of family welfare is needed to monitor the progress of development programs that have been carried out. The fuzzy logic of the Tahani model is one method that can be applied to classify things. The aim of this research is to classify the level of welfare of families as potential recipients of assistance based on population data held by the Mentawai Social Service & P3A. This research was processed using Fuzzy Tahani logic. Fuzzy Tahani is an optimization algorithm that can be used to support decisions by utilizing relational databases. Based on the research results obtained, fuzzy logic with the Tahani model can be used to process family data in accordance with indicators of family welfare levels by providing output in the form of family classification. It's just that the application of the Tahani model should be done on a single rule search function, not to process all the rules using a Tahani query to produce a family classification
IMPLEMENTASI SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN ALAT KONTRASEPSI DENGAN METODE AHP DAN TOPSIS (STUDI KASUS DI PUSKESMAS GUNUNG LABU) Refina Afindania, Pipin; Defit, Sarjon; Sumijan
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 12 No 1 (2024): TEKNOIF APRIL 2024
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2024.V12.1.1-9

Abstract

The problem that is often faced is that many mothers of couples of childbearing age do not understand how to choose a contraceptive method that is suitable for use. To address this problem among couples of reproductive age in choosing the most appropriate contraceptive method, the Analytical Hierarchy Process  (AHP)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is proposed to be utilized. It is expected to be beneficial in aiding the selection of a suitable contraceptive method for users. The objective of this research is to implement the AHP-TOPSIS method in a decision support system for choosing contraceptive methods for couples of reproductive age at the Gunung Labu Community Health Center. The results of the analysis using the AHP-TOPSIS method indicate that the appropriate contraceptive methods for couples of reproductive age are Implan, IUD, Birth Control Injection, and Birth Control Pills. The combination of AHP-TOPSIS in contraceptive method selection yields the conclusion that the Decision Support System (DSS) built in this research is expected to facilitate midwives in recommending contraceptive methods for couples of reproductive age. AHP method is employed to calculate the weights of each contraceptive method criterion. The results of the priority weight calculations for all criteria used in this study yielded a Consistency Index (CI) of 0.07. The analysis using the AHP-TOPSIS method resulted in Implan, IUD, Birth Control Injection, and Birth Control Pills being identified as the appropriate contraceptive methods for couples of reproductive age.
Customized Convolutional Neural Network for Glaucoma Detection in Retinal Fundus Images Islami, Fajrul; Sumijan; Defit, Sarjon
Jurnal Penelitian Pendidikan IPA Vol 10 No 8 (2024): August
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i8.7614

Abstract

Glaucoma is one of the leading causes of permanent blindness and remains a current challenge in the field of ophthalmology. This research aims to present a comprehensive investigation into the development and evaluation of new technology for glaucoma detection in retinal fundus images. The development and evaluation are presented on a customized architecture, using the Convolutional Neural Network (CNN) method. The proposed CNN architecture is designed to address the complex characteristics of glaucoma changes in the identification process. The research dataset consists of 506 retinal images categorized into 117 glaucoma images, 19 suspected glaucoma images, and 370 healthy images. Through our in-depth exploration, we conducted a careful analysis to uncover patterns and fundamental trends related to glaucoma-related features. During the training phase, the proposed CNN achieved outstanding average accuracy, sensitivity, and specificity values of 92.88%, 94.66%, and 89.31%, respectively. In the unseen test dataset, the model demonstrated competitive performance with an accuracy of 80.87%, sensitivity of 85.65%, and specificity of 71.26%. These findings emphasize the potential of the model as a reliable tool for glaucoma detection. The results indicate that the proposed method utilizing a customized CNN architecture is designed for glaucoma detection in retinal fundus images. The presented output results also hold promise for clinical relevance and can be considered an improvement in the care of retinal fundus patients.
Co-Authors Aal, Defrizal Adawiyah, Quratih Ade, Ade Puspita Sari Aditra Agustin, Riris Akbar, Muhamad Rafi Aktavera, Beni Aldi, Febri Auriga, Wira Billy Hendrik Budiantoro, Hendro Budiarti, Lela dedi irawan Defit , Sarjon Deti Karmanita Devi Yuliana, Devi Dewi, Dian Cyntia Dinul Akhiyar Dwiprihatmo, Mohammad Reza Enggari, Sofika Eriyanto, Joko Fadillah, Riszki Fajrul Islami Feriantano Sundang Pranata Feriantano Sundang Pranata Geni, Bias Yulisa Gunadi Widi Nurcahyo Habdi, Habdi Hakim, Wijaya Hendrik, Billy Ilham Asy'ari Jufriadif Na`am, Jufriadif Julius Santony Lubis, Siti Sahara Meiditra, Irzon Mulyanda, Sandy Nst, Ely Nurhalizah Nurcahyo , Gunadi Widi Okmarizal, Bisma Pipin Refina Afindania Pulungan, Akhiruddin Putri, Yolan Ananda Rais, Edo Rinaldi Refina Afindania, Pipin Resnawita Ridho, Ridho Afwan Ridwan, Ridwan Ruri Hartika Zain Sarjon Defit Sarjon Defit Satria Satria, Satria Sovia, Rini Supriyanto, Boby Suri, Ghea Paulina Surmayanti Syafri Arlis Syaputra, Eka B. Syofneri, Nandel Ulfa, Ulia Vidyanti, Angela Yani, Zulfitri Yanti, Salma Nofri Yuda, Fitra Yuda Yuhandri