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All Journal Jurnal Technopreneur (JTech) Jurnal Sistem Informasi dan Informatika (SIMIKA) Andragogi: Jurnal Pendidikan Islam dan Manajemen Pendidikan Islam Journal of Intelligent Decision Support System (IDSS) Nusantara Hasana Journal Journal Automation Computer Information System (JACIS) Jurnal Mandiri IT Brilliance: Research of Artificial Intelligence Jurnal Minfo Polgan (JMP) Indonesian Journal of Informatics and Research Jurnal Indonesia Sosial Teknologi Journal of Artificial Intelligence and Digital Business Jurnal Masyarakat Madani Indonesia Innovative: Journal Of Social Science Research Jurnal Penelitian Sistem Informasi Jurnal Teknik Informatika dan Desain Komunikasi Visual
Syefudin, Syefudin
STMIK YMI Tegal
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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Application of the haversine formula method to determine the closest distance to a minimarket Muttaqin, Anik; Murtopo, Aang Alim; Syefudin, Syefudin; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.293

Abstract

In a digital era that demands speed and efficiency, determining the closest distance to minimarkets is crucial for consumers and the logistics industry. This study proposes the use of the haversine method to improve the accuracy of distance calculations. Through quantitative and quasiexperimental approaches, this study describes the steps of data collection, pre-processing, and application of haversine formulas. The results demonstrate the reliability of the haversine method in estimating distances accurately, allowing users to make more informed decisions in planning trips or logistics strategies. These findings contribute to the academic literature and field practice by providing a more robust and applicable methodology for determining the closest distance. Keywords: haversine, closest distance, minimarket.
Development of mobile applications for IoT-based room temperature monitoring and control Murtopo, Aang Alim; Amalani, Mukhamad Zulfa Bakhtiar; Syefudin, Syefudin; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.309

Abstract

The Internet of Things (IoT) has become one of the most significant technologies, offering a wide range of innovative solutions to improve efficiency and convenience in various aspects of life. One important application of IoT is in environmental management and control, especially room temperature. This research aims to develop a mobile application capable of monitoring and controlling room temperature with an easy-to-understand user interface and the ability to forecast future temperature needs. Research methods used include experimental approaches, data analysis, and model validation to ensure applications function optimally in real-world conditions. The results showed that the application developed was effective in monitoring room temperature conditions in real-time and was able to adjust the temperature quickly and accurately. The implication of this research is the improvement of user convenience and energy efficiency through the use of IoT technology in everyday life.
Applying certainty factor method to identify diseases in rice plants Nugroho, Bangkit Indarmawan; Miftakhuddin, Ahmad; Syefudin, Syefudin; Gunawan, Gunawan
Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v13i1.310

Abstract

Rice (Oryza Sativa L) is the most important food crop in the world after wheat and corn, as well as the main source of protein for most of the world's population, especially in Asia. The Save Swamps for Prosperous Farmers (Serasi) program in Central Java Territory cannot run well considering the tall capacity of existing rice agriculturists to bargain with bugs and maladies of the rice they plant, so it is essential to make a device within the frame of an master framework for diagnosing rice plant infections.  For this reason, it is very important to be aware of the factors that influence production levels. Disease is one of the most detrimental factors in rice production, where many losses are caused by disease. Each of these diseases generally shows symptoms of the disease suffered before it reaches a more severe and widespread stage, these symptoms can be recognized by carrying out a diagnosis first. This can be done using an expert system. In this research, an expert system was utilized which was made utilizing the certainty figure strategy, with a test of 25 ranchers within the West Tegal Area, Tegal City. From the comes about of the inquire about carried out, it was concluded that with this framework the level of exactness obtained using the posttest contains a esteem of 100%, in other words the framework encompasses a decently tall level of accuracy.
Implementasi Server Dengan Sistem Operasi Linux Debian Sebagai Pendukung Penerimaan Peserta Didik Baru Dengan Virtualbox Di SMK Bina Islam Mandiri Kersana Kabupaten Brebes Nugroho, Bangkit Indarmawan; Surorejo, Sarif; Santoso, Bayu Aji; Murtopo, Aang Alim; Syefudin, Syefudin; Arif, Zaenul; Kurniawan, Rifki Dwi; Karsidin, Karsidin; Adhi Santoso, Nugroho
Jurnal Teknik Informatika dan Desain Komunikasi Visual Vol 4 No 1 (2025): Jurnal Teknik Informatika dan Desain Komunikasi Visual
Publisher : Fakultas Komputer Dan Desain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51792/yn6j9k73

Abstract

Every job that is done using computer network technology, the data that we input has actually entered the server computer. Thus the data that we have entered will be automatically saved to the server computer. The real conditions in the field are that there are several things that may cause the PPDB process at SMK Bina Islam Mandiri Kersana, Brebes Regency to be less effective. The main causes are Human Resources, Hardware, PPDB Process and Software. The design of the computer network that will be used as the object of this study, one of the topologies used is the star topology. The materials for designing the implementation of this server are the server computer, client computer, switch and PPDB application. To be able to connect to a network, here it is necessary to have a server IP address which will later be used to connect to other computers in this case the client computer. Previously, install Linux Debian using the VirtualBox application for the server, then configure the network until finished, connect the server computer to the client computer using the media, namely the UTP cable, after that on the client computer set the IP and open the browser to see the results. So it can be concluded that the server system for accepting new students can be done easily, as long as there is a will and perseverance in making it.
Machine Learning Model for Human Resource Placement in Higher Education Syefudin Syefudin; Rifki Dwi Kurniawan
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6861

Abstract

This study presents the development and evaluation of a machine learning model designed to support human resource (HR) placement decisions in higher education institutions. Using combined personnel data from STMIK YMI Tegal and Politeknik Harber, we built a predictive model to estimate staff attendance at institutional progress reporting events, a critical indicator for performance evaluation and role suitability. The dataset comprised 137 records with six categorical predictors: Position, Homebase, Origin, Tegal_Status, Gender, and Institution. Categorical variables were encoded using label encoding, and a Random Forest classifier was trained using a stratified 75%/25% train-test split. The model achieved a held-out test accuracy of 97.14%, precision of 93.33%, recall of 100%, and F1-score of 96.55%, outperforming baseline models (Logistic Regression and Decision Tree). Five-fold cross validation confirmed robust generalization with an average accuracy of 91.22%. Feature importance analysis revealed Position as the most influential variable (76.88% importance), followed by Homebase and Origin. The results suggest that machine learning, particularly ensemble based methods, can provide reliable decision support tools for HR managers in academic settings, enabling data driven placement strategies. This research highlights the potential of predictive analytics for optimizing staff assignments and fostering institutional effectiveness. Future work should include larger datasets, additional features, and external validation to enhance model generalizability.
Penerapan Algortima Convolutional Neural Network (CNN) Untuk Identifikasi Lahan Kosong Di Kota Tegal Berdasarkan Citra Google Earth Mohammad Amin Triwinanto Triwinanto; Aang Alim Murtopo; Syefudin Syefudin; Gunawan Gunawan
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.13626

Abstract

Lahan kosong memiliki berbagai macam jenis. Setiap jenis lahan kosong memiliki macam-macam tertentu dengan model yang beragam. Dalam menentukan jenis lahan kosong maka perlu dilakukan sebuah klasifikasi dengan menggunakan metode Convolutional Neural Network (CNN). Dengan penggunaan CNN dapat dilakukan ekstraksi sebuah fitur kemudian fitur-fitur tersebut akan menjadi data dalam menentukan klasifikasi jenis lahan kosong. Data gambar lahan kosong yang dikumpulkan dari data augmentasi adalah sebanyak 120 gambar dengan jenis lahan kosong tambak, rawa, pemukiman, dan sawah. Keempat kelas jenis lahan kosong tersebut memiliki perbandingam data latih 70% dan data uji 30%. Masing-masing kelas menggunakan empat convolutional layer dengan filter 32, 32, 64, dan 64 dan menggunakan pool size sebesar 2x2 dengan neuron (hidden layer) sebanyak 512. Pengujian website image classification dengan menggunakan metode confusion matrix didapatkan akurasi sebesar 80,5% dari pengujian yang dilakukan pada data uji.
Pengembangan Sistem Check-in Tamu Digital Berbasis Web untuk Monitoring Kunjungan pada Dinas Kominfo Kabupaten Tegal Saputra, Rizqy Mulya; Wiyono, Slamet; Murtopo, Aang Alim; Syefudin, Syefudin
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 3 (2026): Agustus : JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i3.4493

Abstract

Manual guest books in public offices often create incomplete visitor records, slow visit-history retrieval, weak host approval control, limited real-time visibility of active visitors, and difficulty in producing accountable reports. This study develops a web-based digital guest check-in system for monitoring visits at the Communication and Informatics Office of Tegal Regency. The research applies the waterfall model covering requirement analysis, system design, implementation, testing, and maintenance planning. The system includes guest self check-in, WhatsApp number validation, ID-card OCR assistance, host selection, host approval, visit status monitoring, in-app notification, Role-based Access Control, audit log, report export, and emergency attendance. The implementation uses Next.js 15, React, TypeScript, and Tailwind CSS on the frontend, NestJS on the backend, Socket.IO for realtime communication, and a relational data design prepared for PostgreSQL and Prisma ORM. Functional testing showed that login, guest check-in, form validation, OCR correction, approval, visit search, check-out, overstay marking, notification, reporting, user management, host management, endpoint handlers, and emergency attendance worked according to expected outputs. The developed system improves operational traceability, reduces dependence on paper records, strengthens role-based control, and provides faster visit information for front office, host, security, administrator, and leadership roles.
Co-Authors Aang Alim Murtopo Adhi Santoso, Nugroho Agustina, Catur Puji Amalani, Mukhamad Zulfa Bakhtiar Andriani, Wresti Arif, Zaenul Ariyani, Rizki Bangkit Indarmawan Nugroho Fadli, Muhamad Rizaldi Satrio Farkhan, Muhammad Gunawan Gunawan Gunawan Karsidin, Karsidin Kurniawan, Rifki Dwi Miftakhuddin, Ahmad Mohammad Amin Triwinanto Triwinanto Mohammad Rizal Kurniawan Muchamad Nauval Azmi Muhammad Fahmi Muttaqin, Anik Nia Uli Nadia Nursahid, Wahyu Nursidik, Maulia Prima Octarina Pramesti Rifki Dwi Kurniawan Riyadi, Fajar Sugeng Santoso, Bayu Aji Santoso, Nugroho Adh Santoso, Nugroho Adhi Saputra, Rizqy Mulya Sarif Surorejo Supratman, Ardhi Surur, Misbahu Ubaidillah, Muhamad Rizal Wiyono, Slamet