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All Journal Jurnal Transformatika Jurnal Informatika Upgris Jurnal Sains dan Informatika Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Ilmiah Sinus Building of Informatics, Technology and Science Joined Journal (Journal of Informatics Education JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JITU : Journal Informatic Technology And Communication TIN: TERAPAN INFORMATIKA NUSANTARA Information System Journal (INFOS) Bulletin of Computer Science Research Arcitech: Journal of Computer Science and Artificial Intelligence Adopsi Teknologi dan Sistem Informasi Journal of Data Insights Tematik The Indonesian Journal of Computer Science
Setiaji, Galet Guntoro
Universitas Semarang
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Pengembangan PDPT Berbasis Framework Dengan Teknologi Web Service SOAP dan REST di Universitas Semarang Whisnumurti Adhiwibowo; Galet Guntoro Setiaji; Tirta Jurista Kumkamdhani
Jurnal Informatika Upgris Vol 5, No 2: Desember (2019)
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v5i2.4209

Abstract

One indication of the development of a tertiary institution can be seen from the number of students applying from year to year. The increasing number of students is an identification that the tertiary institution is in high demand by the community. University student data is stored in a university database or often called PDPT. PDPT data is used for various things, such as calculating the ratio of lecturers and students, calculating the number of graduations, calculating class ratios to university income. Because of the importance of the PDPT data, the validity of the PDPT data must be maintained. The problem that arises at the University of Semarang is that the data is only contained in the central server and is not distributed well globally or in detail up to the Study Program and the Quality Assurance Agency. This requires an alternative way to validate student data so that student data can be monitored continuously. The method can be done by using software development methods using a framework. Data obtained through the computer center by utilizing a web service so that data can be retrieved directly.
Perbandingan Algoritma Klasifikasi untuk Prediksi Cacat Software dengan Pendekatan CRISP-DM Nurtriana Hidayati; Joko Suntoro; Galet Guntoro Setiaji
Jurnal Sains dan Informatika Vol. 7 No. 2 (2021): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v7i2.313

Abstract

Proses prediksi cacat software merupakan bagian terpenting dalam sebuah pengujian kuliatas software sering juga disebut dengan software quality yang bertujuan untuk mengetahui mutu software dalam pemenuhan kebutuhan fungsional dan kinerjanya. Metode machine learning mempunyai kinerja lebih baik untuk menemukan cacat software daripada metode manual. Algoritma klasifikasi dalam machine learning yang pernah digunakan untuk prediksi cacat software antara lain k-Nearest Neighbor (k-NN), Naïve Bayes (NB) dan Decision Tree (CART). Dalam penelitian ini akan dibandingkan kinerja antara algoritma - algoritma klasifikiasi yaitu k-NN, NB, dan CART untuk prediksi cacat software dengan pendekatan CRISP-DM. CRISP-DM merupakan model proses data mining dengan 6 tahapan yaitu: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, dan Deployment dalam menentukan perbandingan algoritma klasifikasi dalam memprediksi cacat software. Software Matrix yang digunakan pada penelitian ini adalah tujuh dataset dari NASA MDP. Hasil penelitian menunjukkan bahwa nilai rata-rata akurasi algoritma CART lebih baik daripada algoritma k-NN dan NB dengan nilai 0,867. Sedangkan nilai rata-rata akurasi algoritma k-NN dan NB masing-masing 0,859 dan 0,778.
PENINGKATAN KEMAMPUAN PENGELOLAAN WEBLOG DENGAN KONTEN INTERNET SEHAT SEBAGAI SARANA PUBLIKASI DAN INFORMASI PADA SISWA SMA NEGERI 2 SEMARANG Galet Guntoro Setiaji; April Firman Daru; Saifur Rohman Cholil
TEMATIK Vol 2, No 1: July 2020
Publisher : TEMATIK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/tmt.v2i1.1828

Abstract

Community Service Activities (PkM) "Increasing the Capability of Management of Healthy Weblogs with Internet Content as a Means of Publication and Information for Students of Semarang 2 High School" involving partners of High School Middle School students in the city of Semarang. Many school students have not used the weblog to provide information and publications about activities related to learning, school organization activities and other activities. In this activity, partners will be given training in making weblogs with healthy internet content by the PKM team tailored to the level of needs and resources of each partner. At the end of this activity, it is expected that all students who take part in the training can create a weblog by filling out healthy internet content for the better so that it will improve  the  ability  of  publications  and  information  to  internet  users  as  a  form  of  community  service. The purpose of this service is to increase the ability of weblogs to share knowledge, publications and information of  students by creating a healthy internet on the weblog.
Komparasi Metode Naive Bayes dan C4.5 Pada Klasifikasi Persalinan Prematur Hanif, Mohammad Burhan; Rani, Handini Arga Damar; Rifai, Ahmad; Setiaji, Galet Guntoro
Joined Journal (Journal of Informatics Education) Vol 5 No 1 (2022): Volume 5 Nomor 1 (2022)
Publisher : Universitas Ivet

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31331/joined.v5i1.2242

Abstract

Persalinan prematur adalah persalinan diluar masa lahir bayi yang menyebabkan kematian bagi bayi serta komplikasi terhadap ibu bayi. Juga menjadi beban tenaga medis dengan tren peningkatan sebanyak 8%. Klasifikasi data mining hadir sebagai pemecah masalah deteksi pencegahan awal persalianan premature. Dengan memanfaatkan algoritma klasfikasi C4.5 dan algoritma naïve bayes yang dianggap baik secara kinerja. Untuk memilih algoritma terbaik dalam klasifikasi persalinan prematur maka harus diukur dengan baik kinerjanya. Dari hasil pebandingan algoritma naïve bayes dengan algoritma C4.5 didapatkan akurasi sebesar 98.75% dengan AUC 0.5. Sedangkan capaian dari algoritma naïve bayes sebesar 81.88% dan AUC 0.945. Maka dari hasil perbandingan nilai akurasi kedua algoritma tersebut disimpulkan bahwa algoritma C4.5 mampu lebih unggul dalam penanganan data persalianan premature dibandingakan dengan algortima naïve bayes.
Perbandingan Kinerja RNN dan CNN Dalam Klasifikasi Sentimen Ulasan Pengguna Aplikasi di Play Store Saputra, Satria Nugraha; Setiaji, Galet Guntoro; Widiyanto, Max Teja Ajie Cipta
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6408

Abstract

The public frequently shares their thoughts and opinions on various topics, such as products, public figures, or government policies, through online platforms. The process of analyzing review data is referred to as sentiment analysis. This study aims to compare the performance of two deep learning models Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) in classifying user sentiments across five review categories from the Google Play Store: design, photography, gaming, social media, and streaming. Choosing the right algorithm is essential to achieving optimal accuracy, given the variations in language and expression patterns within reviews. The dataset used in this study consists of 50,000 reviews with an imbalanced distribution of positive and negative sentiments. To address this imbalance, oversampling techniques were applied using the Synthetic Minority Oversampling Technique (SMOTE). The evaluation process measured each model's accuracy and loss levels. The results show that CNN consistently outperformed RNN across most categories. For the design category, CNN achieved the highest accuracy of 85% with a loss value of 0.41, compared to RNN, which achieved 83% accuracy and a loss of 0.53. On the other hand, the streaming category showed the lowest performance, with CNN achieving an accuracy of 69% and a loss of 0.63, while RNN achieved 67% accuracy with a loss of 0.72. These findings highlight CNN's superior effectiveness in sentiment analysis across diverse user review categories.
Pengembangan Sistem Antrian Sesuai Jadwal Praktik Dokter Berbasis Website Menggunakan Laravel Hafiq Ibnu Wardana; Galet Guntoro Setiaji; Ahmad Rifa'i
Adopsi Teknologi dan Sistem Informasi (ATASI) Vol. 4 No. 1 (2025): Adopsi Teknologi dan Sistem Informasi (ATASI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/atasi.v4i1.2908

Abstract

Pelayanan kesehatan yang efisien sangat penting, namun sistem antrian manual di rumah sakit seringkali menyebabkan waktu tunggu lama, ketidaknyamanan pasien, dan kesulitan bagi petugas dalam mengelola waktu praktik dokter. Sistem manual ini juga dapat menimbulkan ketidakadilan, terutama bagi pasien BPJS. Penelitian ini mengembangkan sistem antrian berbasis website yang terintegrasi dengan jadwal praktik dokter menggunakan framework Laravel. Sistem ini bertujuan untuk meningkatkan efisiensi waktu tunggu pasien, memberikan transparansi, dan mempermudah pengelolaan antrian oleh petugas. Pasien dapat mendaftar, memilih dokter, dan memantau status antrian secara real-time, sementara petugas dapat mengelola jadwal dokter dan antrian dengan lebih efisien. Pengujian menggunakan metode Black Box menunjukkan hasil yang berhasil pada skenario pengujian seperti pendaftaran pasien, pemilihan dokter, dan pencetakan tiket antrian. Diharapkan, sistem ini dapat mendorong transformasi digital di bidang kesehatan dan memberikan solusi bagi masalah antrian manual di fasilitas kesehatan.
Implementasi Payment Gateway Pada Aplikasi Toko Mebel Menggunakan MERN Stack Hakim, Adya Abdu Azizul; Setiaji, Galet Guntoro; Rifa'i, Ahmad
Jurnal Ilmiah SINUS Vol 23, No 2 (2025): Vol. 23 No. 2, Juli 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v23i2.946

Abstract

The advancement of technology in the e-commerce sector has driven traditional stores to transition to digital systems in order to enhance transaction efficiency and product management. Mebel Amanah is a furniture store owned by Mr. Hj. Wahyono, located in Plebean, Batang. Previously, Mebel Amanah only accepted in-person payments, which proved to be less effective, particularly for customers who were unable to visit the store directly. Although bank transfer options were available, the store provided only one bank account number, posing challenges for customers using different banks due to additional administrative fees. To address this issue, an online payment system using Midtrans was implemented, offering various payment methods such as ATM/bank transfer, credit/debit cards, e-wallets, and virtual accounts. The Furniture Store Application also includes a product management feature that displays product names, prices, descriptions, and stock information in real-time. This application was developed using the MERN Stack and follows the Waterfall development model, which includes system requirement analysis, system design, implementation, testing, deployment and maintenance phases. The implementation results demonstrate that integrating the Midtrans payment gateway improves payment flexibility and speeds up transaction processes. Furthermore, the product management feature allows customers to easily check product availability without needing to inquire directly. In conclusion, the furniture store application not only facilitates transaction processes but also supports the digital transformation of furniture business operations.
Analisis Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Sentimen Ulasan Aplikasi E-Commerce Pecaro, R Immanuel Giovanni Italiano; Setiaji, Galet Guntoro; Rifa'i, Ahmad
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8145

Abstract

The growing digital technology has driven the rapid growth of E-Commerce applications, which is characterized by the number of similar applications available on the Google Play Store. This phenomenon has led to an increase in people's need for convenience in online shopping, as well as showing increasingly fierce competition in the digital marketplace. Reviews on E-Commerce applications in the Google Play Store often serve as a basis for users to decide whether to download an application. These reviews provide valuable insights, allowing users to assess whether the application is worth downloading. Shopee, one of the largest E-Commerce applications in Indonesia, currently has more than 15 million ratings and reviews on the Google Play Store. This study aims to compare the performance of the K-Means and K-Medoids algorithms in clustering numerical data from application reviews. Clustering was performed using the Clustering technique based on two numerical variables, namely score and thumbsupcount, to provide an initial overview of user opinion trends regarding the application. The dataset, consisting of 500 reviews, was collected from the Google Play Store in December 2024. The results Davies-Bouldin Index of the study indicate that K-Means outperforms K-Medoids, with a comparison score of 0.457 to 0.803.
Meningkatkan Kinerja Decision Tree C4.5 dengan Seleksi Fitur Korelasi Pearson pada Deteksi Penyakit Diabetes Mohammad Burhan Hanif; Galet Guntoro Setiaji
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3087

Abstract

Diabetes sebuah penyakit yang menjadi momok seluruh dunia. Kerugianya tidak hanya pada penderita sendiri tetapi juga merambah ke banyak sektor. Baik di sektor pelayanan kesehatan dan sektor financial yang sangat menjadi beban tinggi yang perlu ditangani dengan baik dengan jalan pendeteksian penyakit diabetes sejak dini. Salah satu pendeteksian dini penyakit diabetes dapat memanfaatkan algoritma machine learning pada bidang data mining. Algoritma C4.5 merupakan algoritma machine learning yang memiliki tingkat akurasi dan kecepatan perhitungan tinggi dalam klasifikasi. Namun demikian algoritma C4.5 terganggu dengan data tak seimbang dan fitur data berdimensi tinggi. Pemanfaatan seleksi fitur menjadi salah satu penyelesain masalah data berdimensi tinggi. Algoritma Korelasi Pearson memiliki kemampuan dalam mengukur informasi antar fitur dan diterapkan dalam penelitian ini. Penggunaan Korelasi Pearson dianggap berhasil dalam meningkatkan kinerja algoritma C4.5 dalam deteksi awal penyakit diabetes. Keberhasilan ini terlihat pada hasil akurasi sebesar 95.31% tanpa korelasi pearson menjadi 96.16% dengan pemanfaatan korelasi pearson.
Pengembangan Aplikasi Keuangan Berbasis Web Menggunakan Laravel Filament di PT Kargo Transkontinental Retno Utami, Tri; Galet Guntoro Setiaji; Rifa'i, Ahmad
JITU Vol 9 No 2 (2025)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v9i2.2100

Abstract

PT Kargo Transkontinental, as a company engaged in the field of freight forwarding with a high transaction volume, faces significant operational challenges due to its reliance on manual financial recording processes. This dependence leads to delays in the preparation of financial reports, which can take up to two working days, and carries a potential error rate of up to 10% in cost recording. Such conditions pose a major obstacle to conducting accurate and timely profitability analysis. This study aims to design and develop a web-based financial application using the Laravel Filament framework to overcome these inefficiencies. To ensure a systematic development process, the research adopts the waterfall development method, which consists of the stages of requirements analysis, design, implementation, testing, and maintenance. System requirements were gathered through direct interviews with the company's finance division. The study produced a functional web application that successfully integrates modules for employee income, expenses, and loans, further enhanced with an innovative digital receipt feature to bolster accountability. The application features role-based access control for superadmins and staff and offers centralized reporting capabilities. In conclusion, the developed application streamlines the job costing process, elevates data accuracy, and accelerates financial reporting, thereby supporting more effective decision-making.
Co-Authors Aditya Rizky Purnama Ahmad Rifa'i Ahmad Rifai Ahmad Rifa’i April Firman Daru Astrid Novita Putri Badroe Zaman Basworo Ardi Pramono Bernandika Reyhan Groovytala Cholil, Saifur Rohman Djoko Handoko Galih Setiarso Gibran Masta Pangestu Baskoro Hafiq Ibnu Wardana Hakim, Adya Abdu Azizul Handini Arga Damar Rani Hanif, Mohammad Burhan Joko Suntoro Khoirudin Khoirudin Krida Pandu Gunata Maugy Al Kautsar Mohammad Burhan Hanif Mohammad Burhan Hanif Muhammad Dedi Setiawan Muhammad Rizal Naufal Hanif Athallah Nurtriana Hidayati Pecaro, R Immanuel Giovanni Italiano Retno Utami, Tri Rifa'i, Ahmad Rio Eko Saputro Saputra, Satria Nugraha Septiano Cepeda Da Costa Soiful Hadi Surono Surono Syahrul Adi Saputra Tirta Jurista Kumkamdhani Tirta Kumkamdani Vensy Vydia Vensy Vydia Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit Widiyanto, Max Teja Ajie Cipta