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All Journal Multitek Indonesia : Jurnal Ilmiah JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JSAI (Journal Scientific and Applied Informatics) Jurnal ABDINUS : Jurnal Pengabdian Nusantara Academia Open Indonesian Journal of Cultural and Community Development Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) International Journal of Engineering, Science and Information Technology Bulletin of Information Technology (BIT) Indonesian Journal of Innovation Studies PELS (Procedia of Engineering and Life Science) Procedia of Social Sciences and Humanities MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Prosiding University Research Colloquium Physical Sciences, Life Science and Engineering Indonesian Journal of Applied Technology Journal of Technology and System Information Journal for Technology and Science Tepak Sirih : Jurnal Pengabdian Kepada Masyarakat Madani
Setiawan, Hamzah
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ONION CRACKERS SALES FORECASTING USING ARTIFICIAL NEURAL NETWORK METHOD AND HOLT'S DOUBLE EXPONENTIAL SMOOTHING setiawan, hamzah
MULTITEK INDONESIA Vol 18, No 1 (2024): Juli
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v18i1.7501

Abstract

Changes in product demand is a problem that is often faced by the industry as well as one of them is onion crackers. Tapioca flour is the main ingredient used to make onion crackers. Because the demand for crackers is always changing, this company often experiences excess or shortage of raw materials. If there is an excess of raw materials, the company must incur additional costs for the maintenance and storage of raw materials so that raw materials can be properly stored in accordance with existing standards, which of course costs a lot. Therefore, companies must plan to solve this problem by planning raw material requirements by forecasting raw material requirements using the artificial neural network method and double exponential smoothing holt. The results showed that the artificial network method had a mean square error of 0.120 and the mean square error using the double exponential smoothing method yielded a value of 206.19. Based on these two values, it can be concluded that the artificial neural network method is more accurate than the double exponential smoothing holt method. This can be seen by comparing the roat mean square error values of the two methods..
Application of Data Mining Using the Support Vector Machine (SVM) Method to Analyze Fashion Retail Products to Determine Trends Setiawan, Hamzah
Academia Open Vol 9 No 1 (2024): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.9.2024.8581

Abstract

This study addresses the escalating volume of research by proposing an efficient research storage system through data mining-based categorization. Employing the Support Vector Machine (SVM) method on a dataset comprising 541,910 retail product purchases, the research achieves a significant 96.2% accuracy in categorization using the cross-entropy loss function. The SVM method proves instrumental in systematically organizing research based on fields, methods, and outcomes, showcasing its efficacy in large-scale research storage and organization. This study highlights the SVM's potential as a vital tool for governments and private organizations to enhance access and utilization of research information. The results underscore the positive impact of SVM in overcoming the complexity of research storage on a broader scale, contributing to the advancement of efficient research management systems. Highlights: Efficient SVM Data Management: Proposes SVM-based data mining for effective research information storage. 96.2% Accuracy in Categorization: SVM with cross entropy achieves high accuracy in classifying research data. Organized Access for Better Utilization: SVM organizes research systematically, enhancing accessibility and utilization for government and private sectors. Keywords: Support Vector Machine, Data Mining, Dataset, Retail.
Web-Based E-Tatib Information System Case Study at State Vocational High School 1 Jabon Sidoarjo Sistem Informasi E-Tatib Berbasis Web Studi Kasus di SMK Negeri 1 Jabon Sidoarjo Hasan, Jamal; Rosid, Mochamad Alfan; Eviyanti, Ade; Setiawan, Hamzah
Journal for Technology and Science Vol. 1 No. 1 (2024): Journal for Technology and Science
Publisher : PT ANTIS INTERNATIONAL PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ipteks.v1i1.96

Abstract

This research was conducted because the need for an increasingly stringent Standard Operating Procedure (SOP) in the industrial world. With the existence of SOPs in the world of education industry, of course, they have to match the needs according to existing standards. SMK Negeri 1 Jabon Sidoarjo formed a Team of Rules (Tatib) which implemented SOPs in accordance with Industrial World standards. Handling related to Tatib is still done manually using a book. Of course, with manual recording, efficiency in processing time and reporting is constrained if needed in a short time. The recording process begins with the admin entering the master data required by the information system, then the teacher enters the recording of student violations. The final process for the Counseling Guidance (BK) team can see the number of range points that have been stored and will then make reports outside the information system. That way the process makes the work of the BK team more accurate and faster
Optimization of Feature Selection on Student Complaint Data Using Recursive Feature Elimination to Improve Academic Service Quality Setiawan, Hamzah; Senja Fitrani, Arief; Rahmawati, Yunianita; Wirabumi Putra, Cakra; Usqi Salsabila, Firdausi
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.707

Abstract

This study aims to optimize the feature selection process on student complaint data regarding academic services in universities using the Recursive Feature Elimination (RFE) method. Student complaints' diverse and complex nature requires in-depth analysis to identify crucial features that affect service satisfaction. An accurate feature selection process can help universities understand the most frequently reported issues, enabling them to respond and improve services more effectively. The research utilizes complaint data from various aspects of academic services, such as administration, facilities, and faculty interactions. After preprocessing the data to remove noise and irrelevant entries, RFE is applied to select the most relevant features. Subsequently, a classification model is built using the selected features to identify the most significant complaint patterns. Model evaluation is conducted through cross-validation techniques to ensure accuracy and reliability, with metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that the RFE method significantly enhances model performance in selecting essential features, making the classification model more efficient and accurate in predicting student complaints. Thus, this study contributes significantly to assisting universities in enhancing the quality of academic services through a more targeted analysis of student complaints. Implementing this method will improve the complaint-handling process and increase overall student satisfaction
Sistem Penilaian terhadap Kinerja Guru Berbasis Web (Studi Kasus: SMP Negeri 2 Sukodono) Pralaya, Gilang; Setiawan, Hamzah
Physical Sciences, Life Science and Engineering Vol. 1 No. 2 (2024): Maret
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/pslse.v1i2.202

Abstract

Salah satu program sekolah adalah peningkatan kualitas dalam bidang pendidikan siswa yang berupa rapor yang berfungsi untuk menilai kemampuan siswa. Sedangkan dalam penelitian ini siswa menilai kinerja guru saat proses belajar mengajar selesai. Tujuan penelitian ini adalah untuk memberikan kemudahan para bapak/ibu guru melihat kinerjanya selama mengajar dikelas pada website tersebut dan membantu kepala sekolah dalam melihat kualitas guru di SMP Negeri 2 Sukodono. Dalam penelitian ini, penulis mengambil sampel sebanyak 204 orang siswa kelas VII di SMP Negeri 2 Sukodono pada semester genap tahun pelajaran 2022/2023. Metode penelitian yang digunakan adalah metode kuantitatif. Untuk mengumpulkan informasi yang valid, penulis melakukan observasi, tinjauan pustaka dan wawancara di SMP Negeri 2 Sukodono. Untuk menghitung korelasi antara hasil jawaban dari respoden dengan skala likert yang tersedia, penulis melakukan perhitungan dengan rumus penilaian responden dengan skor maksimal disimbolkan huruf X dengan skor sebesar 4 dengan kategori sangat baik. Skor tersebut dikalikan dengan total pertanyaan yang diberikan yaitu X = 4 x 23 = 92. Berikutnya yaitu skor harapan yang disimbolkan huruf Y dengan menggunakan jumlah responden, lalu dapat dituliskan dengan Y = 92 x 204 = 18.768. Dengan begitu kesimpulan hasil analisis yang menunjukkan bahwa kinerja guru di SMP Negeri 2 Sukodono dengan nilai 82,43% yang mencakup kriteria sangat baik.
Optimalisasi Pengelolahan Jaringan Dengan Pembatasan Bandwidth dan Blokir Akses Tertentu Pada PT Laxo Global Akses Dengan Menerapkan Metode NDLC Ramdansyah, Adiffanani; Setiawan, Hamzah; Indahyanti, Uce; Eviyanti, Ade
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8165

Abstract

This research focuses on optimizing network management at PT Laxo Global Akses by implementing bandwidth restrictions and restricting access to non-work related websites and applications. Internet usage in the company was previously unrestricted, resulting in decreased productivity and unstable network performance, especially during peak hours. To address these issues, this research applies the Network Development Life Cycle (NDLC) methodology and utilizes MikroTik devices for configuration. Key features such as Queue Tree for bandwidth distribution, Firewall Filtering for access control, and Hotspot User Management for user authentication were implemented. The redesigned network topology allows for more structured traffic management and real-time monitoring. The test results show that all test scenarios have succeeded as expected, so it can be concluded that the network configuration and policies implemented in the NDLC process were 100% successful at the implementation and testing stages.
EDUKASI DIGITAL DI SMPN 2 PACET: MENINGKATKAN LITERASI UNTUK MENCEGAH KEJAHATAN SIBER Setiawan, Hamzah; Nuraini, Intan; Aulia Aliffiandi, Rizca; Grahita Albarika, Ayu; Sinta Nuriyah, Rizky
Tepak Sirih : Jurnal Pengabdian Masyarakat Madani Vol. 4 No. 1 (2025): Tepak Sirih : Jurnal Pengabdian Kepada Masyarakat Madani
Publisher : LPPM Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/jpmm.v4i1.3273

Abstract

Kegiatan pengabdian masyarakat ini diselenggarakan di SMPN 2 Pacet dengan tema "Teknologi untuk Kebaikan, Internet Tanpa Kejahatan". Kegiatan ini bertujuan untuk meningkatkan kesadaran dan pemahaman masyarakat, khususnya generasi muda, mengenai pemanfaatan teknologi secara positif dan bertanggung jawab. Ruang lingkup kegiatan mencakup seminar dan sosialisasi yang membahas berbagai topik utama, seperti pemanfaatan teknologi untuk kebaikan, keamanan internet, etika dalam berinternet, dan literasi digital. Metode yang digunakan dalam kegiatan ini adalah pendekatan edukatif melalui seminar interaktif dan diskusi kelompok. Peserta kegiatan terdiri dari siswa, guru, orang tua, dan tokoh masyarakat. Data yang dikumpulkan berasal dari hasil observasi serta evaluasi pemahaman peserta sebelum dan sesudah kegiatan. Hasil evaluasi menunjukkan peningkatan pemahaman peserta terkait pentingnya penggunaan teknologi secara bijak, dengan indikator meningkatnya kesadaran akan ancaman dunia digital serta kemampuan mengidentifikasi dan menghindari kejahatan siber. Kesimpulan dari kegiatan ini menegaskan bahwa edukasi mengenai teknologi dan internet sangat diperlukan bagi masyarakat, terutama generasi muda, guna menciptakan lingkungan digital yang aman, nyaman, dan bermanfaat bagi semua. Keberhasilan kegiatan ini menunjukkan bahwa pendekatan edukatif berbasis seminar dan diskusi efektif dalam meningkatkan literasi digital. Rekomendasi ke depan adalah memperluas cakupan kegiatan ke lebih banyak sekolah serta memperdalam materi mengenai keamanan digital.
Prediksi Harga Tiket Pesawat Domestik Rute Perjalanan Surabaya-Jakarta Menggunakan Metode Regresi Linear Berganda Assara, Enggi Sabrilla; Setiawan, Hamzah; Suprianto
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.1975

Abstract

Air transportation is highly favored for its time efficiency and comfort, especially on busy routes such as Surabaya–Jakarta. However, the dynamic fluctuation of airline ticket prices often makes it difficult for consumers to plan their trips. This study aims to develop a predictive model for airline ticket prices on the Surabaya–Jakarta route using the multiple linear regression method. A total of 10,000 rows of data were analyzed using statistical approaches and analytical processes based on Google Colaboratory, involving stages such as data import, preprocessing, variable transformation, data splitting (training and testing), and classical assumption testing. The resulting regression model demonstrated excellent performance with an R-squared value of 96.4%, indicating that most of the price variation could be explained by independent variables such as airline, departure time, travel duration, baggage capacity, and service type. Violations of assumptions such as normality and heteroskedasticity were addressed through logarithmic transformation and the use of regression with robust standard errors. Furthermore, multicollinearity was minimized using Ridge Regression. Model evaluation showed no signs of overfitting and produced stable prediction results. Only a few variables were statistically significant, highlighting the importance of analyzing variable contributions to enhance model efficiency. The predictive model developed in this study provides accurate and practical results, making it useful for consumers in travel planning and for airlines in developing more competitive pricing strategies.
Humanoid object detection moving in open space using YOLOv8 Yansah, Muhammad Kahfi; Dijaya, Rohman; Setiawan, Hamzah; Sumarno, Sumarno
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 15 No. 2 (2025): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v15i2.60-71

Abstract

This study explores the application of the YOLOv8 algorithm in detecting humanoid objects in an open space environment, with a special focus on school areas such as parking lots. The main objective is to develop an intelligent system that can accurately identify students based on four uniform classifications: none, grey, batik, and department-specific uniforms. The system is designed to function effectively in real-time by analyzing image and video data. The research methodology begins with data acquisition using CCTV footage, followed by annotation and preprocessing using Roboflow. The dataset consists of 314 images with 1,649 labeled bounding boxes, which are then divided into training and validation sets. A yaml configuration file is created to interact with the YOLOv8 model. Training is performed using YOLOv8s variants, with experimental variations in image size, batch size, and epochs to optimize model performance. The evaluation results show that the model achieves a precision of 0.86, a recall of 0.92, and a mean Average Precision (mAP@0.50) of 0.93. Furthermore, visual testing confirms the system's ability to detect students with a total detection accuracy of 85%. Some minor errors were observed in distinguishing between visually similar classes, such as batik and department uniforms. These results demonstrate the robustness and reliability of YOLOv8 in dynamic real-world environments. This study concludes that YOLOv8 can be effectively applied to educational settings for surveillance or monitoring systems. Future research will focus on improving accuracy by expanding the dataset and incorporating more diverse categories of humanoid objects.
ONION CRACKERS SALES FORECASTING USING ARTIFICIAL NEURAL NETWORK METHOD AND HOLT'S DOUBLE EXPONENTIAL SMOOTHING setiawan, hamzah
MULTITEK INDONESIA Vol 18 No 1 (2024): Juli
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v18i1.7501

Abstract

Changes in product demand is a problem that is often faced by the industry as well as one of them is onion crackers. Tapioca flour is the main ingredient used to make onion crackers. Because the demand for crackers is always changing, this company often experiences excess or shortage of raw materials. If there is an excess of raw materials, the company must incur additional costs for the maintenance and storage of raw materials so that raw materials can be properly stored in accordance with existing standards, which of course costs a lot. Therefore, companies must plan to solve this problem by planning raw material requirements by forecasting raw material requirements using the artificial neural network method and double exponential smoothing holt. The results showed that the artificial network method had a mean square error of 0.120 and the mean square error using the double exponential smoothing method yielded a value of 206.19. Based on these two values, it can be concluded that the artificial neural network method is more accurate than the double exponential smoothing holt method. This can be seen by comparing the roat mean square error values of the two methods..
Co-Authors Abidin, Husnul Ade Eviyanti Adi Putra, Lutfi Alshaf Pebrianggara Amelia Kusumawardani, Paramitha Amelia, Paramitha Arief Wicaksono, Arief Arif Senja Fitrani Arisandi, Ricky Renaldo Asiddiq, Afnizar Maulana Assara, Enggi Sabrilla Astutik, Ika Ratna Indra Aulia Aliffiandi, Rizca Aziziyah, Ismi Anisa Duwi Rahayu Grahita Albarika, Ayu Hasan, Jamal Hindarto Imanda, Almyra Gitta Intan Nuraini Mochamad Alfan Rosid Muhammad Fikri Muhammad, Fajar Muhammad, Khithoh Sabda Nur Maslikhatun Nisak Nuril Lutvi Azizah Paramitha Amelia Kusumawardani Pralaya, Gilang Pratiwi, Rosa Machmuda Ramdansyah, Adiffanani Ribangun Bamban Jakaria Rohman Dijaya RR. Ella Evrita Hestiandari sandy, Mochamad septa Senja Fitrani, Arief Sinta Nuriyah, Rizky SITI CHOLIFAH Sumarno . Suprianto1, Suprianto Taurusta, Cindy Triwahono, Handi Uce Indahyanti Usqi Salsabila, Firdausi Wirabumi Putra, Cakra Wiwik Sumarmi Yansah, Muhammad Kahfi Yunianita Rahmawati, Yunianita