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All Journal Jurnal Sains dan Teknologi Teika PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Teknologi Informasi dan Ilmu Komputer CESS (Journal of Computer Engineering, System and Science) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Jurnal Ilmiah KOMPUTASI JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System SYUKUR (Jurnal Inovasi Sosial dan Pengabdian Masyarakat) Jurnal Informatika Universitas Pamulang INTECOMS: Journal of Information Technology and Computer Science JurTI (JURNAL TEKNOLOGI INFORMASI) JURIKOM (Jurnal Riset Komputer) Jurnal Riset Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Journal of Information Systems and Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Madaniya Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Kajian Ilmiah Community Empowerment Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Journal of Applied Computer Science and Technology (JACOST) BEMAS: Jurnal Bermasyarakat KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Jurnal Atma Inovasia Abdimas Indonesian Journal INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Borobudur Informatics Review Jurnal Informatika Polinema (JIP) Jurnal Ilmiah Sistem Informasi Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Dedikasi Nusantara
Sukmasetya, Pristi
Universitas Muhammadiyah Magelang
Religion Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences Other

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Sistem Klasterisasi Volume Sampah Organik di Kota Magelang menggunakan K-Means Nurrohman, Muhamad; Maimunah, Maimunah; Sukmasetya, Pristi
TEMATIK Vol 10 No 1 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v10i1.1338

Abstract

Waste is one of the most serious problems faced by many regions. In Magelang City alone, 70 tons of waste is generated per day, and 60% of it is organic waste. Business sectors such as restaurants, culinary tours, markets, and hotels are high producers of organic waste. Organic waste can be utilized and sorted to be useful for maggot farmers, compost and biogas makers, but the lack of information about the source and availability of this waste is an obstacle for organic waste users and the Environmental Service. The purpose of this research is to assist the Environmental Agency in planning waste transportation, as well as making it easier for organic waste users to get information about the waste. In this study, the data used is waste volume data and data on the number of visitors and traders in hotels, markets, and street vendors in Magelang City, the data is then processed using the K-Means clustering algorithm. The data that has been processed produces the optimal number of clusters is 2, cluster 1 is a low waste volume producing category, while cluster 2 is a high waste volume producing category. After obtaining the clustering results using the K-Means algorithm, an evaluation of the results was carried out using the silhouette score method which resulted in a score value of 0.66, from the evaluation results it can be concluded that the application of the K-Means algorithm in clustering the volume of organic waste in Magelang City is quite good. With these results, it is hoped that it can help the Magelang City government, especially the Magelang City Environmental Agency, maggot cultivators, and other organic waste users to more easily obtain information about the availability of organic waste, which is expected to help reduce the volume of organic waste.
Implementasi Agile Software Development dalam Perancangan Sistem Pengelolaan Limbah Sampah Imam Saputra; Pristi Sukmasetya; Ardhin Primadewi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1379

Abstract

Waste is the residual material from human activities that has no utility; as a result, it can have various negative impacts. However, the issue of waste can be addressed through one method, which is a waste bank. One example of the implementation of a waste bank is in the Karang Hamlet. The waste bank management process at the Cempaka Waste Bank in the Karang Hamlet is still done manually by recording all processes and data in books. This is highly susceptible to errors or mistakes when recording data and requires a significant amount of time for data input and retrieval. To address these issues, the researcher aims to develop a waste management system based on a website to assist and streamline waste bank operations at the Cempaka Waste Bank in the Karang Hamlet. The data collection methods used include observation, interviews, and a literature review, and the software development method utilizes the Agile Software Development method because it is a lightweight and suitable approach for this research. Before testing with potential users, the system is initially tested using black-box testing, and the results show that the system functions properly, and its features work as intended
Arsitektur Convolutional Neural Network untuk Model Klasifikasi Citra Batik Yogyakarta Arya Prayoga; Maimunah; Pristi Sukmasetya; Muhammad Resa Arif Yudianto; Rofi Abul Hasani
Journal of Applied Computer Science and Technology Vol 4 No 2 (2023): Desember 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i2.486

Abstract

Batik is an Indonesian culture that has been recognized as a world heritage by UNESCO. Indonesian batik has a variety of different motifs in each region. One area that is famous for its batik motifs is Yogyakarta. Yogyakarta has a variety of batik motifs such as ceplok, kawung, and parang which can be differentiated based on the pattern. Yogyakarta batik motifs need to be preserved so they do not experience extinction, one way is by introducing Yogyakarta batik motifs. The recognition of Yogyakarta batik motifs can utilize technology to classify images of Yogyakarta batik motifs based on patterns using the Convolutional Neural Network (CNN). The Yogyakarta batik motif images used for classification totaled 600 images consisting of 3 different motifs such as ceplok, kawung, and parang. Image classification using CNN depends on the architectural model used. The CNN architecture consists of two stages, namely Convolutional for feature extraction and Neural Network for classification. The CNN architectural models made for the introduction of Yogyakarta batik motifs totaled 7 models which were distinguished at the feature extraction stage. The highest accuracy results in the classification of Yogyakarta batik motif images using CNN were obtained in the 6th model. The 6th model has an accuracy of 87.83%, an average precision of 88.46% and an average recall of 87.66%. The accuracy, precision, and recall values ​​obtained by the 6th model are above 80%, which means that the 6th model can classify Yogyakarta batik motifs quite well.
Application of digital marketing in the efforts to develop MSMEsin Baleangung Village, Grabag District, Magelang Regency Pristi Sukmasetya; Muliasari Muliasari; Amelia Anggraini; Famila Zidda; Hasna Nur Arifaini; Avian Ali Mas'ud
BEMAS: Jurnal Bermasyarakat Vol 4 No 1 (2023): BEMAS: Jurnal Bermasyarakat
Publisher : LPPMPK-Sekolah Tinggi Teknologi Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/bemas.v4i1.596

Abstract

Baleagung Village is one of the villages in Grabag District, Magelang Regency which has several MSMEs such as Cilok Baraya, Griya Sangkrip bird cages and FEED Concentrate. However, MSME actors still depend only on direct marketing (face to face), even thoughthe era is increasingly digital. This is due to the lack of knowledge and doubts by MSME actors about existing technology to help their marketing. Seeing this, the purpose of this community service activity is to provide MSME actors with the knowledge and courage to be able to apply digital marketing in their marketing so that they can help expand their reach. The method used is digital marketing outreach and training, creating digital marketing platforms, mentoring activities, to monitoring and evaluation. The results ofthis activity are increased knowledge, a desire to progress, and the ability of MSME actors to carry out marketing so that in the end the resulting products can be recognized by a wider audience and have a greater opportunity to develop existing markets
Peningkatan Kualitas Media Pembelajaran Dengan Google Sites Pada Guru SMK 1 Windusari Magelang Hasani, Rofi Abul; Yudatama, Uky; Yudianto, Resa Arif; Sukmasetya, Pristi; Maimunah, Maimunah
Jurnal Pengabdian kepada Masyarakat UBJ Vol. 5 No. 2 (2022): June 2022
Publisher : Lembaga Penelitian Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/j3vvyy86

Abstract

Technology has changed the teaching and learning process in the world of education. The most visible change is the use of learning media in schools. The presence of digital media provides a variety of educational innovations, where rigid and monotonous conventional learning will be replaced by learning using digital media which is considered more practical, flexible, and not limited by space and time. One of the learning media is a website. Based on the results of interviews with teachers at SMK Negeri 1 Windusari, they said the importance of online learning media, especially when online learning demands. Therefore, in this PKM, the authors carry out google sites training activities to improve the ability of teachers of SMK Negeri 1 Windusari to create learning media. After training on making teaching media using google sites will help teachers in making interesting learning materials and conveying them to students. So that this activity will provide good benefits to teachers, students, and SMK N 1 Windusari, Magelang Regency. Many teachers were previously reluctant to use making websites because of difficulties. However, after this training, the teachers at SMK Negeri 1 Windusari began to be enthusiastic about making learning media using google sites. Because using google sites is quite easy for teachers to do.
Pengaruh Data Preprocessing terhadap Imbalanced Dataset pada Klasifikasi Citra Sampah menggunakan Algoritma Convolutional Neural Network Resa Arif Yudianto, Muhammad; Sukmasetya, Pristi; Abul Hasani, Rofi; Sasongko, Dimas
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2575

Abstract

Garbage is one of Indonesia's most significant problems with an increase in waste each year reaching 187.2 million tonnes/year. Various efforts to reduce the amount of waste such as Garbage Banks have been encouraged. However, this program has not run well, because some people have difficulty distinguishing the type of waste. One solution to overcome this problem is that need a system that can classify the type of waste. The deep learning approach with the CNN algorithm is currently widely used to solve classification problems. This method requires a large number of datasets to increase the level of accuracy. Getting a garbage dataset is a particular problem in the training process because the dataset is unbalanced. The dataset used amounted to 2527 data consisting of 6 classes. Several treatments such as undersampling and image augmentation are applied to overcome imbalanced datasets. Other treatments such as the type of input image channel and the use of filters are combined into 24 experimental scenarios to achieve the highest accuracy. The results of the experiment get the best scenario, namely, the dataset is undersampling and then augmented with 5 geometric transformation parameters with the input image being RGB and applying a sharpening filter to get an accuracy value of 0.9919 with 20 epochs.
Pengenalan Deteksi Wajah Artificial Intelligence dan Achievement Motivation Training untuk Siswa SMK Kuncup Samigaluh Sukmasetya, Pristi; Primadewi, Ardhin; Yudianto, Muhammad Resa Arif; Maimunah, Maimunah; Hasani, Rofi Abul; Nugroho, Setiya
Jurnal Atma Inovasia Vol. 4 No. 3 (2024)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

— Artificial Intelligence (AI) is a field of computer science aimed at developing machines capable of performing tasks that typically require human intelligence. In recent years, the development of AI has shown significant progress, and its use has expanded across various sectors, including education. The application of AI in education offers various opportunities and challenges, such as personalized learning and enhancing students' skills, but also presents challenges in technological adaptation and ethical understanding. This paper discusses the utilization of AI-based facial recognition technology at SMK Kuncup Samigaluh, with the goal of enhancing students' competence in information technology. This community service activity involves a series of structured stages, including initial planning, activity implementation, discussion and Q&A, as well as evaluation and feedback. The results of this activity indicate a significant improvement in students' understanding of AI and facial recognition technology, as evidenced by the increase in post-test scores compared to pre-test scores. With an interactive demonstrative approach, this activity successfully provided a positive impact on students' knowledge and interest in AI, and broadened their horizons regarding career opportunities in information technology.
Perancangan UI/UX Website Teknik Informatika UNIMMA Menggunakan Metode Design Thinking Pangestiaji, Yongki; Hendradi, Purwono; Sukmasetya, Pristi
CESS (Journal of Computer Engineering, System and Science) Vol 9, No 1 (2024): January 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i1.55112

Abstract

Website merupakan sistem yang sangat penting bagi banyak orang guna menggali suatu informasi. Tampilan yang baik memiliki nilai dan daya tarik tersendiri bagi pengguna. Universitas Muhammadiyah Magelang (UNIMMA) memiliki banyak website sistem informasi masing-masing pada setiap prodi untuk mempermudah berjalannya proses perkuliahan terutama prodi Teknik informatika. Tujuan dari penelitian ini adalah untuk merancang dan memperbaharui tampilan (UI) dan (UX) website tersebut. Metodologi yang digunakan pada penelitian ini adalah design thinking, yang melibatkan analisis kebutuhan pengguna, memetakan masalah, menghasilkan ide, membuat prototype, dan melakukan pengujian. Untuk tahap pengujian, 10 peserta diberikan kuesioner System Usability Scale (SUS) untuk menilai website teknik informatika. Hasilnya menunjukkan rata-rata skor SUS sebesar 86,25 yang menunjukkan tingkat kepuasan pengguna yang tinggi dan keselarasan dengan kebutuhannya. Temuan-temuan ini memberikan landasan yang kuat untuk perbaikan dan kemajuan lebih lanjut pada situs web, memastikan bahwa situs ini dapat terus memberikan pengalaman pengguna yang optimal dan memenuhi kebutuhan pengguna dengan lebih baik. 
Analisis Pengelompokan UMKM Berdasarkan Kategori Menggunakan Algoritma K-Means dan K-Medoids Ajarwiro, Cweto Bolodiko; Maimunah, Maimunah; Sukmasetya, Pristi
CESS (Journal of Computer Engineering, System and Science) Vol 9, No 1 (2024): January 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i1.54632

Abstract

Sektor Usaha Mikro Kecil dan Menengah (UMKM) menjadi salah satu faktor utama yang mendorong pertumbuhan ekonomi Indonesia. Jumlah kategori UMKM yang banyak perlu dilakukan pengelompokan agar dapat membantu pemerintah dalam mendukung pengembangan UMKM. Dalam penelitian ini dilakukan pengelompokan UMKM berdasarkan kategori menggunakan algoritma K-Means dan K-Medoids. Data yang digunakan dalam penelitian ini adalah data UMKM Kota Magelang yang diambil dari PeRSADA sebanyak 3491. Pada tahap pengolahan data dilakukan pengecekan tipe data, penanganan data yang hilang, pelabelan dan penjumlahan kategori UMKM. Setalah data diolah maka dilakukan pengelompokan data menggunakan algoritma K-Means dan K-Medoids. Pengelompokan kategori UMKM menggunakan algoritmat K-Means dan K-Medoids menghasilkan 3 klaster. Pengelompokan menggunaka K-Means menghasilkan klaster tinggi sebanyak 1 kategori, klaster sedang 3 kategori, dan klaster rendah 60 kategori. Pengelompokan menggunakan K-Medoids menghasilkan klaster tinggi 1 kategori, klaster sedang 2 kategori, dan klaster rendah 61 kategori. Berdasarkan nilai DBI, algoritma K-Means mempunyai nilai 0,496 sedangkan algoritma K-Medoids bernilai 0,499. Dengan demikian klastering UMKM Kota Magelang menggunakan K-Means lebih baik daripada algoritma K-Medoids. Melalui pengelompokan UMKM berdasarkan kategori dapat membantu memberikan informasi untuk pengembangan UMKM.
Prediksi Jumlah Sampah di TPSA Menggunakan Pendekatan Machine Learning Almira, Venia; Maimunah, Maimunah; Sukmasetya, Pristi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7278

Abstract

The amount of waste at landfills is increasing along with the growing population and human activities. Predicting the amount of waste has become one of the ways to address waste management issues. The quantity of upcoming waste can be determined through waste prediction, providing essential information for solving waste-related problems. This research involves modeling daily waste predictions using three machine learning algorithms: Linear Regression, Support Vector Regression, and Random Forest Regressor. The data used in this study is the waste data at Banyuurip landfill, Magelang City, covering the period from 2019 to 2022. In the data processing stage, attributes for data usage are selected, daily waste summation is performed, missing values are handled, and normalization is carried out using min-max normalization. The three machine learning algorithms are employed in the prediction modeling stage to obtain optimal parameters. The prediction model is evaluated by calculating the MSE. The results of the three waste prediction models using Linear Regression show a model with an MSE-train of 0.0086 and an MSE-test of 0.0083, while the RMSE-train is 0.0930 and the RMSE-test is 0.0915. The optimal SVR prediction model is obtained with hyperparameter combination C = 1, gamma = 1, and epsilon = 0.05, yielding MSE-train of 0.0030 and MSE-test of 0.0089, with RMSE-train at 0.0556 and RMSE-test at 0.0943. The Random Forest Regressor model results in a model with n_estimators of 500, random_state of 1, without the hyperparameter max_depth, and has MSE-train of 0.0012 and MSE-test of 0.0081, along with RMSE-train at 0.0353 and RMSE-test at 0.0901. Based on these three models, it is concluded that the best model is the Random Forest Regressor with the smallest MSE and RMSE values.
Co-Authors Abul Hasani, Rofi Aditya Prasetyawan Afidah, Inayatun Najihatul Afif Prasetyo Agung Vinia Rahma Agus Setiawan Ahmad Husen Ardiyansah Ajarwiro, Cweto Bolodiko Aji Purwoko Aji, Ridho Catur Novi Alan Kusuma Aliudin, Habib Said Almas Nurfarid Budi Prasetyo Almira, Venia Alvine Candra Amelia Anggraini Annisa Annisa Shabilla Arazka Firdaus Anavyanto Ardhin Primadewi Arham Rahim Arrojak, Muhamad Yusril Arthalia Wulandari, Ika Arya Prayoga Asmanto, Budi Athoetan, Salma Atmaja, Audi Ilham Auzi Asfarian Avian Ali Mas'ud Basunondro, Wibiartono Bayu Agustian Budi Setyo Wulan Cahya Sonny Surachman Catur Rahmawati Catur Rahmawati Catur Wulandari Dana I. Sensuse Danu Rendra Krisna Meganatara Devi Oktaviani Dimas Sasongko Dwihantoro, Prihatin Dwika Oktavian Eko Muh Widodo Elin Cahyaningsih Emilya Ully Artha Endah Ratna Arumi Endah Ratna Arumi, Endah Ratna Erzi Hidayat Fadhlillah Jatmiko Utomo Famila Zidda Febriyanto, Yusril Firdaus Anavyanto, Arazka Haryanto, Taufiq Hasani, Rofi Abul Hasbi Hutauruk1, Dzakiyyah Hasna Nur Arifaini Heni Apriyani Herlin Lutfiannisa, Alifia Hery Setiawan Hidayat, Chandra Nur Hidayat, Erzi Ika Arthalia Ika Arthalia Wulandari Imam Saputra Iqbal Ridwan Darmawan Jihan Nuariputri Laela Dian Angraeni Lusi Nurlatifah Maimunah Maimunah Maimunah Maimunah, Maimunah Maulida, R.Bima Gofiruli Muhammad Hafizh Hamdanuddinsyah Muhammad Resa Arif Yudianto Muhammad Riyan Andriyanto Muhammad Riyan Andriyanto Mujito Mukhtar Hanafi Muliasari Muliasari Nafiah, Anisatun Nawangsari, Rosiska Syekhrum Noris Mohd Norowi Nurrohman, Muhamad Nuryanto Nuryanto Pangestiaji, Yongki Pradana Putra Utomo Purwono Hendradi Purwono Hendradi Putri Winly Apriliani Rahendra Firman Sunartama Ramadhan, Dean Apriana Rayinda Faizah Resa Arif Yudianto Resa Arif Yudianto Resa Arif Yudianto, Muhammad Resa Arif Yudiyanto Reza Pradana Adiharsa Rochiyanto, Andi Rofi Abul Hasani Sadewi, Fungki Ayu Safira Ayu Muthi'ah Salam, Muhammad Ifsyaus Salsadila Arsuliyanti Sandi Satria Alamsyah Sari, Nur Ita Sari, Wina Permana Satrio Satrio Sensuse, Dana I. Setiawan, Agus Setiya Nugroho Slamet Hidayat Sophia Ikhsanti Sugiarto, Bagus Susanti, Dwi Tito Rizki Purnomo Tri Wahyuni Uky Yudatama Uky Yudatama, Uky Utuh Setyaning Janji Wachiddin M Huda Wahyu Santoso Wina Permana Sari Wisnu Nugroho Wulandari, Ika Arthalia Yantria Gusta Nugraha Yudianto, Resa Arif Yusril Febriyanto Yusril Febriyanto Zahwa Dwi Larasati