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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System JURNAL MEDIA INFORMATIKA BUDIDARMA JTERA (Jurnal Teknologi Rekayasa) INOVTEK Polbeng - Seri Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Advance Sustainable Science, Engineering and Technology (ASSET) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Community: Jurnal Pengabdian Pada Masyarakat Jurnal Rekayasa elektrika INOVTEK Polbeng - Seri Informatika Prosiding SENTIMETER : Seminar Nasional Teknologi Informasi, Mekatronika, dan Ilmu Komputer
Abu Salam
Universitas Dian Nuswantoro
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Aplikasi Genetika Untuk Penjadwalan Mata Pelajaran di SMAN 3 Semarang Restu Agung Pamuji; Junta Zeniarja; Abu Salam
JOINS (Journal of Information System) Vol 4, No 1 (2019): Edisi Mei 2019
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (769.453 KB) | DOI: 10.33633/joins.v4i1.2550

Abstract

Merencanakan kegiatan belajar mengajar di sekolah sangat penting dan rumit. Ini bukan masalah serius ketika sekolah memiliki sejumlah kecil kelas dengan minimal pertemuan belajar mengajar. Tetapi itu akan menjadi masalah dalam hal jumlah kelas, ruang dan jumlah guru yang terbatas. Contoh masalah yang sering muncul adalah sulitnya menempatkan slot waktu untuk menghindari bentrokan. Untuk alasan ini, diperlukan suatu aplikasi untuk membangun sistem perencanaan dengan meminimalkan kesalahan perencanaan sehingga kegiatan pembelajaran dapat dilakukan secara optimal. Metode penjadwalan pelajaran ini menggunakan pendekatan algoritma genetika. Algoritma genetika merupakan pendekatan komputer yang diinspirasi oleh teori genetika untuk menyelesaikan masalah yang memerlukan optimasi. Hasil penerapan algoritma genetika sebagai pendekatan untuk mengoptimalkan perencanaan mata pelajaran sekolah telah menghasilkan nilai fitness yang optimal. Kemudian diuji dari faktor correctness, menghasilkan sejumlah error hingga 0 baris. Diuji secara fungsional tidak menghasilkan fungsi primer dan sekunder yang tidak berfungsi dengan benar. Diuji dengan faktor portabilitas dalam mencoba berbagai aplikasi, dapat bekerja dengan baik di semua lingkungan.Kata kunci : Penjadwalan , Algoritma Genetika
Implementasi Algoritma K-Means Dalam Pengklasteran untuk Rekomendasi Penerima Beasiswa PPA di UDINUS Abu Salam; Diyan Adiatma; Junta Zeniarja
JOINS (Journal of Information System) Vol 5, No 1 (2020): Edisi Mei 2020
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1772.802 KB) | DOI: 10.33633/joins.v5i1.3350

Abstract

Rekomendasi penerima beasiswa Peningkatan Prestasi Akademik (PPA) dikelompokkan menjadi 2 cluster yaitu diterima dan tidak diterima untuk mendapatkan beasiswa. Algoritma K-Means merupakan teknik unsupervised learning yang dapat digunakan dalam mengelompokkan data pengajuan beasiswa. Tujuan dari penelitian ini adalah untuk merekomendasikan penerima beasiswa dengan menggunakan algoritma k-means, hasil rekomendasi berupa penempatan data pendaftar beasiswa ke masing-masing kelompok cluster yang dihasilkan. Eksperimen proses clustering dilakukan menggunakan data pendaftar beasiswa PPA dari biro kemahasiswaan udinus tahun 2016 sebanyak 441 pendaftar beasiswa PPA. Melalui seleksi atribut, k-means ini melakukan perhitungan untuk menempatkan setiap data ke cluster yang sudah ditentukan. Sebanyak 154 mahasiswa direkomendasikan mendapatkan beasiswa PPA sedangkan 287 mahasiswa tidak mendapatkan. 
Implementasi Algoritma K-Nearest Neighbor Berbasis Forward Selection Untuk Prediksi Mahasiswa Non Aktif Universitas Dian Nuswantoro Semarang Abu Salam; Ferry Bintang Nugroho; Junta Zeniarja
JOINS (Journal of Information System) Vol 5, No 1 (2020): Edisi Mei 2020
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1774.334 KB) | DOI: 10.33633/joins.v5i1.3351

Abstract

Masalah yang muncul berkaitan dengan status mahasiswa salah satunya adalah status mahasiswa yang non aktif. Beberapa faktor penyebab status non aktif tersebut diantaranya adalah faktor ekonomi, kemampuan akademik, dan lain – lain. Manajemen perguruan tinggi perlu mengidentifikasi serta melakukan tindakan terhadap mahasiswa yang mempunyai status “tidak diharapkan” untuk mengetahui faktor munculnya masalah tersebut perlu dilakukan evaluasi saat pertengahan masa studi mahasiswa guna mencegah sedini mungkin munculnya mahasiswa yang diindikasi terdapat status tidak aktif untuk mengurangi dampak yang ditimbulkan akibat status non aktif tersebut. Pada penelitian ini akan dilakukan prediksi mahasiswa non aktif menggunakan algoritma klasifikasi K-Nearest Neighbor yang dikombinasikan dengan metode forward selection untuk seleksi atribut yang diharapkan mampu meningkatkan nilai akurasi pada proses klasifikasi. Nilai akurasi yang didapatkan pada algoritma K-Nearest Neighbor sebesar 96.43% sedangkan pada algoritma K-Nearest Neighbor berbasis Forward Selection sebesar 97.27%. Kata Kunci: Mahasiswa Non Aktif, Forward Selection, K-Nearest Neighbor
Sistem Pakar Diagnosa Penyakit Skizofrenia dengan Forward Chaining dan Bayesian Network Abu Salam; Junta Zeniarja; Riyan Ardiansyah
JOINS (Journal of Information System) Vol 6, No 1 (2021): Edisi Mei 2021
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2056.985 KB) | DOI: 10.33633/joins.v6i1.4371

Abstract

Skizofrenia merupakan gangguan kesehatan jiwa yang menjadi permasalahan masyarakat yang sangat penting serta harus memperoleh perhatian dari pemerintah. Berdasarkan hasil dari Riset Kesehatan Dasar (RisKesDas) pada tahun 2013 di negara Indonesia terdapat 1,7 dari 1000 warga atau kurang lebih 400.000 orang yang menderita penyakit Skizofrenia. Kurang meratanya tenaga kesehatan di bidang kejiwaan memperburuk penanganan yang seharusnya dapat segera dilakukan. Sistem pakar merupakan jawaban yang tepat untuk permasalahan tersebut karena sistem pakar adalah suatu sistem yang dirancang untuk dapat menirukan keahlian seorang pakar atau ahli dalam menjawab pertanyaan  dan memecahkan suatu masalah berdasarkan gejala yang diidap oleh pasien. Sistem pakar ini menggunakan metode forward chaining untuk mendapatkan sebuah kesimpulan dari gejala-gejala skizofrenia yang dimiliki oleh pasien dan bayesian network untuk menghitung seberapa akurat suatu sistem pakar tersebut mengidentifikasi suatu masalah. Sistem ini dibangun menggunkan web dengan  bahasa pemrogaman PHP serta databsae MySQL untuk menyimpan data skizofrenia. Proses pengujian fungsionalitas sistem pakar ini berjalan dengan baik serta tingkat akurasi tiap-tiap gejala mendapatkan hasil diatas 80%.
PENGEMBANGAN SOLAR DRYER DOME UNTUK PENINGKATAN KUALITAS PRODUKSI KOPI Kusmiyati Kusmiyati; Abu Salam; Juli Ratnawati
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 4 No 4 (2021): APTEKMAS Volume 4 Nomor 4 2021
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (510.437 KB) | DOI: 10.36257/apts.v4i4.3529

Abstract

Community service activities with SME Omah Kopi Ngemplak Banyuanyar Village were carried out to increase the capacity and quality of production of ground coffee, green coffee beans, and roasted coffee beans. From the observations of the Service Team to partners, there were two problems. The first problem: Omah Kopi Ngemplak produces coffee using the traditional drying method which relies on sunlight. Drying is one of the most important coffee processing processes because it affects the quality of the coffee beans produced. This drying process takes a long time and has the potential to rot in the rainy season so that farmers' performance is hampered and the quality of coffee beans decreases. The second problem is the limited marketing of coffee where farmers only sell coffee to middlemen and stalls, in addition to coffee packaging which is still very simple. Therefore, to overcome these problems in this service program, two activities were carried out, namely the first manufacture, training, and application of the solar dryer dome dryer. The existence of a solar dryer dome is expected to increase the quantity and quality of coffee production. The second is the creation, training, and marketing assistance with web marketplace-based applications and attractive coffee packaging. The application of an attractive marketplace and packaging is expected to help increase the marketing of coffee products.
Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa Junta Zeniarja; Abu Salam; Farda Alan Ma'ruf
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (650.804 KB) | DOI: 10.17529/jre.v18i2.24047

Abstract

Students are a major part of the life cycle of a university. The number of students graduating from a university often has a small ratio when compared to the number of students obtained in the same academic year. This small student graduation rate can be caused by several aspects, such as the many student activities accompanied by economic aspects, as well as other aspects. This makes it mandatory for a university to have a model that can take into account whether the student can graduate on time or not. One of the main factors that determine the reputation of a university is student graduation on time. The higher the level of new students at a university, with the same ratio, there must also be students who graduate on time. An increase in the number of student data and academic data occurs if many students do not graduate on time from all registered students. So that it will affect the image and reputation of the university which can later threaten the accreditation value of the university. To overcome this, we need a model that can predict student graduation so that it can be used as policy making later. The purpose of this study is to propose the best classification model by comparing the highest level of accuracy of several classification algorithms including Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) to predict student graduation. In addition, the feature selection process is also used before the classification process to optimize the model. The use of feature selection in this model with the best features using 12 regular attribute features and 1 attribute as a label. It was found that the classification model using the Random Forest algorithm was chosen, with the highest accuracy value reaching 77.35% better than other algorithms.
Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa Junta Zeniarja; Abu Salam; Farda Alan Ma'ruf
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i2.24047

Abstract

Students are a major part of the life cycle of a university. The number of students graduating from a university often has a small ratio when compared to the number of students obtained in the same academic year. This small student graduation rate can be caused by several aspects, such as the many student activities accompanied by economic aspects, as well as other aspects. This makes it mandatory for a university to have a model that can take into account whether the student can graduate on time or not. One of the main factors that determine the reputation of a university is student graduation on time. The higher the level of new students at a university, with the same ratio, there must also be students who graduate on time. An increase in the number of student data and academic data occurs if many students do not graduate on time from all registered students. So that it will affect the image and reputation of the university which can later threaten the accreditation value of the university. To overcome this, we need a model that can predict student graduation so that it can be used as policy making later. The purpose of this study is to propose the best classification model by comparing the highest level of accuracy of several classification algorithms including Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) to predict student graduation. In addition, the feature selection process is also used before the classification process to optimize the model. The use of feature selection in this model with the best features using 12 regular attribute features and 1 attribute as a label. It was found that the classification model using the Random Forest algorithm was chosen, with the highest accuracy value reaching 77.35% better than other algorithms.
Implementasi Model Convolutional Neural Network (CNN) pada Aplikasi Deteksi Kanker Kulit Menggunakan Expo React Native Yonismara, Arvie Arvearie; Salam, Abu
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5351

Abstract

Skin is the outermost organ of the human body, serving to protect the internal parts from threats such as sunlight exposure. Excessive exposure to sunlight can potentially cause skin cancer. Over the past decade, the number of skin cancer cases in Indonesia has increased. The most common method for detecting skin cancer is biopsy, which is quite expensive and time-consuming. Considering this issue, a skin cancer detection application using Deep Learning technology is needed to identify skin cancer at an early stage. Therefore, this research aims to develop a skin cancer detection application using Expo React Native and implement a CNN deep learning model to classify seven classes of skin lesions based on the HAM10000 dataset. The performance evaluation of the CNN model used shows a high performance score, with an average overall score of 0.98. Given this performance, the model is feasible and ready to be implemented in a mobile application. This study demonstrates that the skin cancer detection application using Expo React Native is capable of implementing the deep learning model and can be used to detect skin cancer. Based on the results of the application testing using the black box testing method, perfect results were obtained with 100% success precentage. From the four parts of the application, namely select image, open camera, predict image, and delete image that were tested, all four parts demonstrated that the functionality and features of the skin cancer detection application work well
BIG DATA PIPELINE INFRASTRUCTURE DESIGN IN MSME E-COMMERCE SYSTEMS WITH A FOCUS ON DATA SOURCE PROCESSING USING ORCHESTRATION TOOLS Wibowo, Isro' Rizky; Sani, Ramadhan Rakhmat; Dewi, Ika Novita; Alzami, Farrikh; Rizqa, Ifan; Salam, Abu; Irawan, Candra; Aqmala, Diana
Transmisi: Jurnal Ilmiah Teknik Elektro Vol 26, No 1 Januari (2024): TRANSMISI: Jurnal Ilmiah Teknik Elektro
Publisher : Departemen Teknik Elektro, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/transmisi.26.1.48-54

Abstract

In the digital era, Micro, Small and Medium Enterprises (MSMEs) need to utilize data to improve their business performance, such as increasing customer targets, product development and pricing strategies. Apache Airflow is a powerful tool for building data scraping pipelines that are scalable, flexible, and easy to monitor. One of them is the Central Java MSME data scraping pipeline, which collects business registration information, business type, location, contacts, products, and financial information from various websites, including the Central Java Provincial Government website, basic goods price comparison tables, and specialized news sites. The captured data is stored in a data warehouse for further analysis by the Central Java souvenir entrepreneurs association (ASPOO) in the region. Apache Airflow is used to manage the scraping pipeline in the Central Java MSME E-Commerce system and ensure it runs smoothly. Apache Airflow also has a built-in dashboard for monitoring pipelines and troubleshooting issues. Overall, scraping pipeline in the Central Java MSME e-commerce system is a valuable tool for collecting and analyzing data on the MSME sector in Central Java. This pipeline is scalable, flexible and easy to use, and can be adapted to different user needs and can be integrated with various systems.
DEVELOPMENT OF TIME-SERIES-BASED MLOPS ARCHITECTURE FOR PREDICTING SALES QUANTITY IN MICRO, SMALL, AND MEDIUM ENTERPRISES (MSMES) Lesmarna, Salsabila Putri; Alzami, Farrikh; Rizqa, Ifan; Salam, Abu; Aqmala, Diana; Megantara, Rama Aria; Pramunendar, Ricardus Anggi
Transmisi: Jurnal Ilmiah Teknik Elektro Vol 26, No 2 April (2024): TRANSMISI: Jurnal Ilmiah Teknik Elektro
Publisher : Departemen Teknik Elektro, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/transmisi.26.2.64-69

Abstract

Micro, Small, and Medium Enterprises (MSMEs) constitute a significant portion of the economy in many developing countries, playing a vital role in employment generation and economic growth. Sales performance is a critical factor for MSMEs, influenced by various internal and external factors. Time-series analysis offers a valuable tool to predict sales quantities by analyzing historical data and identifying patterns and trends. In this context, the SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous Variables) model emerges as a suitable method to forecast future sales, leveraging both historical data and external variables. This research explores the synergy between time-series analysis, specifically SARIMAX modeling, and MLOps (Machine Learning Operations). Finally, this research aims to provide a framework for the practical application of MLOps to enhance sales forecasting and decision-making processes within MSMEs, fostering their growth and sustainability in a competitive market landscape.
Co-Authors Adhitya Nugraha Alan Ma’ruf, Farda Alpiana, Vika Alzami, Farrikh Anisatawalanita Ukhifahdhina Ardin, Akbar Ilham Ardytha Luthfiarta Arifianti, Fidela Putri Arifin, Muhammad Farhan Astuti, Yani Parti Candra Irawan Catur Supriyanto Catur Supriyanto Damaswara, Silvester Aditya Debrina Luna Arghata Mangkawa Diana Aqmala Dimas Pratama, Yohanes Diyan Adiatma Dzaki, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Erwin Yudi Hidayat Erwin Yudi Hidayat Fahmi Amiq Farda Alan Ma'ruf Ferry Bintang Nugroho Haresta, Alif Agsakli Ifan Rizqa Ika Novita Dewi Iskandar, Deo Andrianto Juli Ratnawati Junta Zeniarja Kahingide, Hastyantoko Dwiki Khafiizh Hastuti Kurniawan, Defri Kurniawan, Wira Adi Kusmiyati Kusmiyati L. Budi Handoko Lesmarna, Salsabila Putri Maulana, Fadhli Faqih Megantara, Rama Aria Moh. Sholik Muhammad Jamhari Mukti, David Ramantya Muljono Muljono Mulyanto, Edy Muzaki, Rizki Nuzul Nabila, Talitha Safa Nafanda, Cynthia Dwi Norman, Maria Bernadette Chayeenee Octaviani, Dhita Aulia Pamungkas, Tahta Arya Paramita, Cinantya Pawidya, Novandra Putra Prinantyo, Gilang Djati Putra, Aditya Herdiansyah Ramadhan Rakhmat Sani Ramadhan, Irfan Surya Restu Agung Pamuji Ricardus Anggi Pramunendar Riyan Ardiansyah Sidiq, Syaiful Rizal Sindhu Rakasiwi Utomo, Danang Wahyu Verdian Putra Wicaksana Wibowo, Isro' Rizky Yonismara, Arvie Arvearie