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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informasi dan Teknologi 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) The Indonesian Journal of Computer Science Jurnal Perangkat Lunak

Sumijan

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Author-ID : 1226735

Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Prediksi Pendapatan Terbesar pada Penjualan Produk Cat dengan Menggunakan Metode Monte Carlo Geni, Bias Yulisa; Santony, Julius; Sumijan
Jurnal Informatika Ekonomi Bisnis Vol. 1, No. 4 (December 2019)
Publisher : SAFE-Network

Completing cat products in meeting consumer demand is something that must be addressed. Sales are very important for sales. The amount of demand for goods increases, it will get a large income. The purpose of this study is to predict the sales revenue of paint products at UD. Masdi Related, makes it easy for the leadership of the company to find out the amount of money obtained quickly. This research also makes it easy for companies to take business strategies quickly and optimally. The data used in this research is the data of paint product sales for January 2016 to December 2018 which is processed using the Monte carlo method. Income prediction will be done every year. In addition to predicting revenue, the sales data is also used to predict product demand every year. To predict the sales of paint products using the Monte Carlo method. The results of this study can predict sales revenue of paint products very well. Based on the results of tests conducted on the system used to predict sales revenue of cat products with an average rating of 89%. With a fairly high degree of accuracy, the application of the Monte Carlo method can be estimated to make an estimate of the income and demand for each paint product every year. Necessary, will facilitate the leadership to choose the right business strategy to increase sales of cat product sales.

Simulasi Monte Carlo dalam Memprediksi Disribusi Kopi Starbuck Irawan, Dedi; Sumijan; Nurcahyo , Gunadi Widi
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 3 (September 2020)
Publisher : SAFE-Network

Distributing and marketing Starbuck coffee is a very important part of the company PT Vision Logistik Transindo in increasing a profit. Starbuck is a company originating from the United States headquartered in Seattle, Washington, and already has the world's largest coffee shop with 20,336 stores in 61 countries, including Indonesia. In distribution management it is very necessary to determine the Starbuck coffee needed, so that customer demand can be fulfilled. This study conducted data on Starbucks coffee distribution from 2017 to 2019. The data processing in this case uses the Monte Carlo algorithm to predict the distribution of Starbuck coffee. In accelerating data processing, this study applies a Web-based program with the PHP programming language (Hypertext Processor). The result of the test is to get the predicted results of Starbuck coffee with an accuracy level of 90%. So that the results obtained can help the company PT.Vision Logistik Transindo in increasing distribution in the coming year.

Simulasi Algoritma Monte Carlo dalam Memprediksi Pendapatan Penjualan Produk Kalsium Tiens Syariah Yani, Zulfitri; Sumijan
Jurnal Informatika Ekonomi Bisnis Vol. 3, No. 1 (March 2021)
Publisher : SAFE-Network

In meeting consumer needs, the availability of calcium products is a problem that needs attention. Items that are often sold will be the largest stock, so that the demand for finished goods is met and income will also increase. This study aims to predict the level of sales revenue for calcium products at stokies tiens 872, in determining which products will be provided for the following year. The data to be used is the sales data for calcium products from 2018 to 2019. Data processing is carried out using the Monte Carlo algorithm, then the data is processed and generated using random numbers taken randomly, after which it is converted or changed in the form of distribution with use simulation techniques to get the results. The results obtained in predicting the income level of sales of calcium products, by making comparisons with the sales data that were sold, namely Nutrient High Calcum Powder that existed in 2018, with simulation results in 2018 with a data accuracy rate of 91%, with the number of calcium products. as many as 1367 products. Then the data in 2019 with simulation results in 2019 with a data accuracy rate of 93% with the number of calcium products as many as 1667 products. With the resulting level of accuracy in the study, the monte carlo simulation can help the stockies predict sales revenue based on processed data, so that this study is able to predict the level of income for the following year.

Simulasi Monte Carlo dalam Mengidentifikasi Peningkatan Penjualan Tanaman Mawar Dewi, Dian Cyntia; Sumijan
Jurnal Informatika Ekonomi Bisnis Vol. 3, No. 2 (June 2021)
Publisher : SAFE-Network

Roses are one of the most popular types of plants in the community. The sale of roses at the flower shop of 5 siblings is increasingly in demand. Identifying the increase in sales is important in analyzing sales progress. At the present time the seller can only see a manual increase in sales that are most in demand. This study aims to determine predictions of the increase in sales of rose flowers with a monte carlo simulation accurately and accurately. The data that will be processed in this study in the last 2 years, namely 2018 and 2019, rose plants obtained at the 5 Brothers Flower Shop in Solok City. There are several types of roses in the predicted sales level. Then the data will be converted into the probability distribution into cumulative frequency and followed by generating random numbers so that they can determine random numbers. Next, we will group the boundary intervals of the random numbers that have been obtained and continue with the simulation process so that the simulation results and percentage accuracy are obtained using the Monte Carlo method. The results of this study on data processing from 2019 to 2020 have an accuracy of 90%. So this research is very appropriate in identifying the increase in sales for the following year. The design of this system determines the amount of increased sales of goods using the monte carlo method in a flower shop of 5 siblings. Monte Carlo simulations can be used to identify specific sales increases. The results obtained are quite accurate using the Monte Carlo method.

Sistem Penunjang Keputusan dalam Penentuan Prioritas Pembanguanan Menggunakan Metode Trus Base dengan Topsis Aktavera, Beni; Sumijan
Jurnal Informatika Ekonomi Bisnis Vol. 2, No. 4 (December 2020)
Publisher : SAFE-Network

Village communities need empowerment in order to develop community welfare and independence to increase attitudes, knowledge, behavior, skills, and priority needs of the village community, including development. villages must be done well in an effort to community village. Village development has an context. Research is to assist Merangin district in making decisions to determine priorities for sub-district development, Merangin Regency which refers to the Regional Long-Term Development Plan (RPJPD). 2018-2019 and 2020 budgets while the decision-making method to solve existing problems is to use increased public participation using a trust-based (trust-based). With the TOPSIS (Technique For Others Preference by Similarity to Ideal Solution) method. With the method to be used, this system can provide information in the form of proposals which are prioritized to be implemented which are aligned with the Regional Long-term Development Plan (RPJPD) and the National Medium Term Development Plan (RPJMN) so that village development can be carried out properly, the TOPSIS method used was able to overcome the weaknesses in the old system and gave 90% accurate results in determining the development priorities of Merangin Regency using the method TOPSIS, and the application of the topsis method for this system could contribute to the results of ranking alternative development priorities in Kab. Merangin to the maximum.

Metode Ekstraksi Fitur Canny, GLCM dan Segmentasi Warna Menggunakan K-Means Clustering Dalam Peningkatan Motif Batik Zain, Ruri Hartika; Sumijan
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4514

Batik merupakan salah satu hasil seni budaya Indonesia berupa kain cetak yang dibuat dengan teknik tertentu. Motif motif bunga melambangkan keindahan dan kebahagiaan. Peneliti mengusulkan metode ekstraksi fitur Canny, GLCM dan segmentasi warna menggunakan k-means clustering dalam peningkatan motif batik. Dengan mengekstraksi fitur dari citra batik yang sudah ada, dapat dihasilkan citra batik dengan motif warna yang lebih banyak. Terlihat dari motif batik yang memiliki tekstur, tekstur dapat dijadikan sebagai salah satu unsur pembeda batik satu dengan yang lain. Penelitian ini juga mengimplementasikan metode Canny, GLCM dan LBP untuk ekstraksi fitur tekstur, HSV colour moment untuk ekstraksi fitur warna, sedangkan metode k-means clustering untuk mengklasifikasikan citra batik dan mengidentifikasi citra batik pewarna alam dan citra batik pewarna sintetis berdasarkan warna. Tujuan dari penelitian ini adalah untuk menggabungkan pola pada data yang sudah ada dengan pola baru. K-means clustering untuk mengelompokkan piksel citra batik digital berdasarkan warna. Hasil penelitian ini menunjukkan bahwa k-means clustering dapat meningkatkan desain batik baru dengan pola dan warna yang berbeda.

Analisis Data Forensik Pada Rekaman CCTV Menggunakan Metode National Institute Of Standard Techology (NIST) Ilham Asy'ari; Yuhandri; Sumijan
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i3.7779

CCTV (Closed-Circuit Television) recordings have become one of the important instruments in monitoring and securing various places such as companies, commercial buildings, public institutions, and households. CCTV recordings are often vital evidence in investigating crimes, accidents, or other incidents. However, in addition to the visual content stored in CCTV recordings, metadata also plays an essential role in forensic analysis and event reconstruction. The NIST method has developed several techniques and guidelines for forensic metadata analysis on CCTV recordings. This research aims to explore and apply the forensic metadata analysis methods recommended by NIST (National Institute of Standards and Technology) in the context of CCTV recordings. By involving forensic data analysis techniques and information security principles, this study will delve into the potential of metadata analysis in supporting criminal investigations, event reconstructions, and meeting the security standards established by NIST. This research is crucial in the context of digital security and modern forensic investigations. The outcome of applying the NIST methods in forensic data analysis of CCTV recordings is the preparation of an official report derived from the stages outlined in the NIST method, so that the report can serve as a reference in court, and the authenticity of the digital evidence can be validated. By applying the NIST method in forensic data analysis of CCTV recordings, the case handling process becomes structured and adheres to procedures, with a valid report ensuring the integrity of the digital evidence.

PENERAPAN MACHINE LEARNING MENGGUNAKAN ALGORITMA DECISION TREE UNTUK PREDIKSI TINGKAT KELULUSAN MAHASISWA Ely Nurhalizah Nst; Sumijan; Gunadi Widi Nurcahyo
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/simika.v8i2.3958

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

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

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.

Co-Authors Aal, Defrizal Adawiyah, Quratih Ade, Ade Puspita Sari Aditra Agustin, Riris Akbar, Muhamad Rafi Aktavera, Beni Auriga, Wira Billy Hendrik Budiantoro, Hendro Budiarti, Lela dedi irawan Deti Karmanita Devi Yuliana, Devi Dewi, Dian Cyntia Dwiprihatmo, Mohammad Reza Ely Nurhalizah Nst Eriyanto, Joko Febri Aldi Feriantano Sundang Pranata Feriantano Sundang Pranata Geni, Bias Yulisa Gunadi Widi Nurcahyo Habdi Habdi Habdi, Habdi Ilham Asy'ari Jufriadif Na`am, Jufriadif Julius Santony Lubis, Siti Sahara Meiditra, Irzon Mulyanda, Sandy Nurcahyo , Gunadi Widi Okmarizal, Bisma Pipin Refina Afindania Pulungan, Akhiruddin Refina Afindania, Pipin Resnawita Ridho, Ridho Afwan Ridwan, Ridwan Riszki Fadillah Ruri Hartika Zain Sarjon Defit Satria Satria, Satria Sovia, Rini Supriyanto, Boby Surmayanti, Surmayanti Syafri Arlis Syaputra, Eka B. Syofneri, Nandel Ulfa, Ulia Vidyanti, Angela Yani, Zulfitri Yanti, Salma Nofri Yuda, Fitra Yuda Yuhandri Yuhandri, Yuhandri

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