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Sistem Pendukung Keputusan Pemilihan Pelanggan Terbaik Menggunakan Metode MOORA Studi Kasus CV Sinar Indah Sejahtera Sahroni; Anief Fauzan Rozi
Journal of Information System and Artificial Intelligence Vol. 3 No. 1 (2022): Journal of Information System and Artificial Intelligence Vol 3 No 1 bulan Nove
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v3i1.110

CV Sinar Indah Sejahtera is one of the businesses engaged in trading/marketing where CV Sinar Prosperity acts as a provider and also as a distributor of basic necessities. For basic necessities, including rice, sugar, salt, peanuts, green beans, and also various other basic necessities. In order to improve the quality of sales and to establish communication and trust in consumers, CV Sinar Indah Sejahtera has the initiative to provide discounts/rebates to the best customers owned by CV Sinar Indah Sejahtera. Based on the above ideas, therefore we need a system that can process customer data which produces the best customer decision output, CV Sinar Indah Sejahtera. One of the roles of a Decision Support System (DSS) is to manage data using certain calculation methods which will produce a recommendation for a decision sequence. In this case CV Sinar Indah Sejahtera will use a decision support system using the MOORA calculation method to determine the best customer. From the test results that have been carried out from 5 alternative data, the best results are Mrs. Afui with a value of 35.9678 and second place is Aseng with a value of 29.5007 and the percentage of system performance is 80% which has been explained in the sub-chapter 4.2.4.2 Validation of Ranking Results with Facts.

Pengembangan dan Pelatihan Sistem PSB di SMK Ma’arif 1 Temon, Kulon Progo, Daerah Istimewa Yogyakarta Anief Fauzan Rozi; Agus Sidiq Purnomo
Jurnal Pengabdian Nasional (JPN) Indonesia Vol. 4 No. 2 (2023): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jpni.v4i2.154

SMK Ma'arif 1 Temon is a private Vocational High School with 3 expertise programs namely Accounting and Institutional Finance, Clinical and Community Pharmacy, and Software Engineering which is located on Raya Wates Purworejo Street, RT/RW : 27/13, Kaliwangan Hamlet, Temon Wetan Village, Temon District, Kulon Progo Regency, Special Province of Yogyakarta. Even though it has resources and one of the expertise in Information Technology (IT), this school does not yet have a new student admissions system (PSB) and so far, it is still using Google forms. Service activities are carried out by creating an information system that can provide convenience for SMK Ma'arif 1 Temon, as well as making a dashboard to display the PSB recapitulation results. With this community service activity in the form of creating a PSB system, it is hoped that it will facilitate data collection and selection of new student admissions at SMK Ma'arif 1 Temon.

Sistem Pendukung Keputusan Penentuan Prioritas Bantuan Stimulan Perumahan Swadaya Menggunakan Metode SMART Ongki Firdian Afandi; Anief Fauzan Rozi
Journal of Information System and Artificial Intelligence Vol. 3 No. 2 (2023): Vol. 3 No. 2 (2023): Journal of Information System and Artificial Intelligence
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v3i2.111

The self-help home assistance program is an annual work program at the Sleman Regency Public Works Department. In determining the recipient of home self-help assistance at the Sleman district public works service, it is still done manually so that a lot of data is not stored properly and even is lost. In this study, the researcher aims to create a system that can assist in determining the recipients of self-help housing in Sleman district so that data can be well documented and the process of determining beneficiaries does not take time. This study uses the SMART (Simple Multi Attribute Rating Technique) method with an assessment of 13 criteria, namely roof covering damage, roof truss damage, column and ring block damage, brick and wall damage, frame damage, window shutter damage, door leaf damage, substructure damage. , damage to the floor covering, damage to the sloof, damage to the bathroom, damage to the bathroom, damage to drains. Based on the results of the study, it can be concluded that the application of decision-making using the SMART (Simple Multi Attribute Rating Technique) method resulted in a 100% match between manual calculations and the system with 75% test data.

Implementasi Sistem Pendukung Keputusan Menentukan Suplier Bahan Baku Minuman Terbaik Menggunakan Metode Smart (Studi kasus Sedot.idn) Awaludin Yusrizal; Anief Fauzan Rozi
Journal of Information System and Artificial Intelligence Vol. 3 No. 2 (2023): Vol. 3 No. 2 (2023): Journal of Information System and Artificial Intelligence
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v3i2.123

Currently, the beverage business competition is getting tougher, making each business owner want to provide the best for customers. Sedot.idn has been doing the selection of raw material suppliers manually so that the work done is less efficient because every time there is a change in price, quality, service and delivery time, sedot.idn business owners have to calculate and re-determine the supplier they will choose. With these problems, it is deemed necessary to make it easier to make work more effective and efficient by creating a system to help Sedot.idn business actors determine the best raw material suppliers for their business with the criteria of quality, price, service, and delivery accuracy. This system will be made using the Simple Multi Attribute Rating Technique or commonly abbreviated as SMART. In this study, data collection and analysis will be carried out to draw conclusions to determine research recommendations for the best minimum raw material suppliers and produce a system that can help business actors Sedot.Idn Making a decision support system with the SMART method can help provide solutions for the head of the outlet owner in choosing the best supplier so that Sedot.idn business owners do not need to manually calculate in the supplier selection process.

Pengembangan dan Pelatihan Sistem PSB di SMK Ma’arif 1 Temon, Kulon Progo, Daerah Istimewa Yogyakarta Anief Fauzan Rozi; Agus Sidiq Purnomo
Jurnal Pengabdian Nasional (JPN) Indonesia Vol. 4 No. 2 (2023): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jpni.v4i2.154

SMK Ma'arif 1 Temon is a private Vocational High School with 3 expertise programs namely Accounting and Institutional Finance, Clinical and Community Pharmacy, and Software Engineering which is located on Raya Wates Purworejo Street, RT/RW : 27/13, Kaliwangan Hamlet, Temon Wetan Village, Temon District, Kulon Progo Regency, Special Province of Yogyakarta. Even though it has resources and one of the expertise in Information Technology (IT), this school does not yet have a new student admissions system (PSB) and so far, it is still using Google forms. Service activities are carried out by creating an information system that can provide convenience for SMK Ma'arif 1 Temon, as well as making a dashboard to display the PSB recapitulation results. With this community service activity in the form of creating a PSB system, it is hoped that it will facilitate data collection and selection of new student admissions at SMK Ma'arif 1 Temon.

Sistem Pendukung Keputusan Penentuan Calon Penerima Bantuan Program Pedagang Menggunakan Metode Evaluation Based On Distance From Average Solution Alhamdhani Harasi; Anief Fauzan Rozi
Journal of Information System and Artificial Intelligence Vol. 4 No. 2 (2024): Vol. 4 No. 2 (2024): Journal of Information System and Artificial Intelligence
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v4i2.183

Improving the welfare of the community is also very important because it cannot be separated from the economic aspect of the community being given a trigger by the government in the form of social assistance in the form of funds with certain objectives, for example for the benefit of traders, in creating community welfare. Of course, the government has done this, but policies regarding the provision of assistance still have to be monitored, criticized, evaluated, and developed. The services provided still include social norms to determine the process of distributing services for the merchant assistance program. In the current problem, namely regarding decision making in determining the recipients of the merchant program assistance, because currently the Dompet Duafa Institution is still determining the recipient of assistance manually. A decision support system or Decision Support System (DSS) is a system that is able to provide capabilities in terms of problem solving and communicating for a problem with semi-structured and unstructured conditions though. The basic principle of the Evaluation based on Distance from Average Solution (EDAS) method is to use two distance measures, namely Positive Distance from Average (PDA) and Negative Distance from Average (NDA). The alternative that has the highest PDA value and the lowest NDA value will be the best alternative.

Sentiment Analysis of Telegram Application User Satisfaction on Google Play Store Using Naïve Bayes, Logistic Regression and SVM Adellia Septiani Putri; Anief Fauzan Rozi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/r6cyb589

Sentiment analysis is a technique for finding out how people feel about something and putting the polarity of text into groups of documents or words so that they can be labeled neutral, positive, or negative. We will use the Naïve Bayes algorithm, logistic regression, and SVM to conduct sentiment analysis on how happy Telegram app users are. The purpose of this study is to see what people who use the app think and group their thoughts into three groups: neutral, positive, and negative. The three methods' results will be compared to see which is most accurate for this study. The results of this sentiment analysis show that many users are dissatisfied with the verification code they need to register or log in to their accounts. This makes it difficult for new users to get the verification code because the app itself sends it. The SVM approach has an accuracy value of 89.73%, which means it is more accurate in this study. The Naïve Bayes approach is accurate by 75.61%, while the logistic regression method is accurate by 87.49%.

Sentiment Analysis and Classification of User Reviews on the Redbus Application Using Logistic Regression And SVM Nafi' Ikhsan Burrhanuddin; Anief Fauzan Rozi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/k6k6m469

The increasing number of RedBus users in Indonesia has led to a growing volume of user reviews on digital platforms, especially the Google Play Store. These reviews reflect user perceptions and are valuable for sentiment analysis. This study aims to classify sentiments in RedBus user reviews using Logistic Regression and Support Vector Machine (SVM) algorithms. A total of 2,000 reviews were collected through automated web scraping and labelled using a lexicon-based approach. The data underwent preprocessing steps including normalisation, tokenisation, filtering, stemming, and labelling. Features were transformed using the TF-IDF method and split into 90% training and 10% testing sets. Evaluation results showed that SVM with a linear kernel outperformed Logistic Regression, achieving 91.10% accuracy and more balanced F1-scores across sentiment classes. Logistic Regression reached 86.39% accuracy but performed lower on positive sentiment. A paired t-test confirmed the statistical significance of the performance difference (p = 0.0005). These findings suggest that SVM is more effective in handling high-dimensional text data and can be recommended for real-world sentiment classification tasks, such as filtering negative reviews and improving customer service.

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