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All Journal Jurnal Informatika dan Teknik Elektro Terapan JOIN (Jurnal Online Informatika) JURNAL ABDIKARYASAKTI Jurnal Penelitian dan Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering
Setiarso, Galih
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Humanities Computer Science & IT Economics, Econometrics & Finance Environmental Science Social Sciences Other

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Anti-Corruption Disclosure Prediction Using Deep Learning Utomo, Victor Gayuh; Kumkamdhani, Tirta Yurista; Setiarso, Galih
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.840

Abstract

Corruption gives major problem to many countries. It gives negative impact to a nation economy. People also realized that corruption comes from two sides, demand from the authority and supply from corporate. On that regard, corporates may have their part in fight against corruption in the form of anti- corruption disclosure (ACD). This study proposes new method of ACD prediction in corporate using deep learning. The data in this study are taken from every companies listed in Indonesia Stock Exchange (IDX) from the year 2017 to 2019. The companies can be categorized in 9 categories and the data set has 8 features. The overall data has 1826 items in which 1032 items are ACD and the other 794 items are non-ACD. In this study, the deep neural network or deep learning is composed from input layer, output layer and 3 hidden layers. The deep neural network uses Adam optimizer with learning rate 0.0010, batch size 16 and epochs 500. The drop out is set to 0.05. The accuracy result from deep learning in predicting ACD is considered good with the average training accuracy is 74.76% and average testing accuracy is 76.37%. However, the loss result isn’t good with average training loss and testing loss are respectively 51.76% and 50.96%. Since the aim of the study to find the possibility of deep learning as alternative of logistic regression in ACD prediction, accuracy comparison from deep learning and logistic regression is held. Deep learning has average prediction accuracy of 76.37% is better than logistic regression with average accuracy of 67.15%. Deep learning also has higher minimum accuracy and maximum accuracy compared to logistic regression. This study concludes that deep learning may give alternatives in ACD prediction compared the more common method of logistic regression.
Implementation of Failover Recursive Gateway and Load Balancing PCC Method on Internet Networks at Universitas Semarang Surono, Surono; Setiarso, Galih; Hadi, Soiful
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6337

Abstract

This study implements and analyzes failover recursive gateway and load balancing using the Per Connection Classifier (PCC) method on the internet network at Universitas Semarang to enhance the reliability and efficiency of bandwidth distribution. The methodology involves using Wireshark for network data testing and analysis. The results demonstrate three main achievements: (1) bandwidth from both ISPs is evenly distributed, (2) the failover system functions optimally by automatically switching to the active link if one ISP fails, and (3) the method effectively balances bandwidth and manages connection disruptions. Before implementation, ping tests to www.google.com showed an average response time of 62 ms, with balanced upload/download speeds of 100 Mbps. After implementation, the ping response remained stable at 62 ms, while upload speeds increased to 158 Mbps and download speeds to 144 Mbps. This study proves that failover recursive gateway and PCC-based load balancing improve network stability and efficiency, providing a reliable solution for bandwidth management and connection continuity at Universitas Semarang.
Pelatihan Pembuatan Presentasi Interaktif Menggunakan Canva untuk Optimalisasi Pembelajaran Siswa SMKN 8 Semarang Setiarso, Galih; Handayani, Sri; Christanto , Febrian Wahyu
Jurnal Penelitian dan Pengabdian Masyarakat Vol. 3 No. 2 (2025): May 2025
Publisher : Yayasan Pondok Pesantren Sunan Bonang Tuban

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61231/jp2m.v3i2.346

Abstract

Community Service by the Faculty of Information and Communication Technology (FTIK) of Semarang University (USM) aims to provide training to students on optimizing the use of the Canva application as a tool for designing interesting and interactive learning materials. The activity method through training which was carried out on Friday, October 18, 2024 was attended by 19 class XII students of the TKJ Department of SMK N 8 Semarang. The results of this activity were measured from the results of the pre-test and post-test, it was obtained that 100% of participants felt helped after participating in the training where students' knowledge and understanding in making designs and presentations with Canva became deeper.
ASISTEN KELAS INTERAKTIF MOODLE BERBASISKAN INTERNET OF THINGS DAN TELEGRAM BOT Hirzan, Alauddin Maulana; Surono, Surono; Setiarso, Galih
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i1.3833

Abstract

Pendidikan adalah aspek kehidupan yang juga menjadi hak asasi dari tiap manusia. Oleh karena itu, pendidikan tidak boleh putus dalam keadaan apapun. Semenjak terjadinya pandemi di seluruh dunia, popularitas platform Moodle sebagai tempat pembelajaran daring meningkat. Namun hal ini tidak diimbangin dengan sumber daya manusianya. Banyak sekali para pengajar senior yang tidak familiar dengan platform tersebut. Oleh karena itu, peneliti mengusulkan untuk mendesain sebuah asisten kelas interaktif berbasiskan Internet of Things dan Telegram Bot untuk membantu para pengajar untuk mengakses Moodle hanya melalui aplikasi berpesan instan. Dari hasil pengujian yang dilakukan, model tersebut telah sukses melayani 117.628 permintaan. Namun juga disertai permintaan dalam antrian mencapai 75.526 permintaan dan permintaan gagal mencapai 3.175 permintaan. Berdasarkan kecepatan waktu respons nya, model dapat melayani 1000 pengguna dengan rata-rata waktu 66,86 detik. Bisa disimpulkan bahwa model yang diusulkan berhasil melayani banyak pengguna meskipun memiliki kegagalan yang tidak signifikan.
PELATIHAN INSTALASI DAN KONFIGURASI PERANGKAT RUMAH PINTAR BERBASIS IOT UNTUK SISWA SMK NEGERI 8 SEMARANG Handayani, Sri; Putri, Astrid Novita; Christanto, Febrian Wahyu; Setiarso, Galih
Jurnal Abdikaryasakti Vol. 6 No. 1 (2026): April
Publisher : Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/ja.v6i1.24018

Abstract

Revolusi industri 4.0 menuntut dunia pendidikan menyesuaikan diri dengan perkembangan teknologi terkini termasuk teknologi Internet of Things (IoT) yang kini banyak diterapkan di berbagai bidang. Terdapat kendala di SMKN 8 Semarang khususnya pada jurusan Teknik Jaringan Komputer dan Telekomunikasi (TJKT) yaitu sebesar 58,3% siswa berdasarkan hasil pre-test masih ragu-ragu dalam pemahaman dan praktik penerapan teknologi IoT secara menyeluruh. Meskipun IoT telah menjadi bagian dari program unggulan sekolah tetapi terdapat keterbatasan fasilitas dan pelatihan teknis yang menghambat kesiapan siswa menghadapi tantangan industri digital. Tujuan kegiatan Pengabdian kepada Masyarakat (PkM) ini adalah untuk meningkatkan pengetahuan dan keterampilan siswa dalam instalasi dan konfigurasi perangkat rumah pintar berbasis IoT menggunakan mikrokontroler Arduino dan sensor. Kegiatan ini mencakup instalasi sensor gerak, pemrograman mikrokontroler, dan simulasi sistem rumah pintar. Metode yang digunakan adalah penyampaian materi interaktif, demonstrasi perangkat, praktik kelompok, dan refleksi hasil. Luaran dari kegiatan PkM ini adalah 100% pemahaman dan kompetensi siswa meningkat dalam hal praktik pembuatan teknologi rumah pintar berbasis IoT. Diharapkan dari kegiatan PkM ini akan terbangun sinergi berkelanjutan antara perguruan tinggi dan SMK dalam penguatan pendidikan vokasi berbasis teknologi.
Co-Authors Alauddin Maulana Hirzan Astrid Novita Putri, Astrid Christanto , Febrian Wahyu Febrian Wahyu Christanto Hadi, Soiful Handayani, Sri Kumkamdhani, Tirta Yurista Surono Surono Victor Gayuh Utomo