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All Journal Jurnal Transformatika Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Teknologi Terpadu Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) TIN: TERAPAN INFORMATIKA NUSANTARA Tematik Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science Jurnal DIMASTIK
Badroe Zaman
Universitas Semarang
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

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Komparasi Metode Klasifikasi Batik Menggunakan Neural Network Dan K-Nearest Neighbor Berbasis Ekstraksi Fitur Tekstur Badroe Zaman; Ahmad Rifai; Mohammad Burhan Hanif
Journal of Information System and Informatics Vol 3 No 4 (2021): Journal of Information Systems and Informatics
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v3i4.213

Abstract

Batik tulis adalah hasil seni budaya yang memiliki keindahan visual dan mengandung makna filosofis pada setiap motifnya. Batik tulis memiliki morif yang sangat beragam dan memiliki tingkat kompleksitas yang tingi sehingga menjadi kesulitan tersendiri dalam pengelompokan kelas batik tertentu. Klasifikasi citra ke dalam kelas tertentu juga menjadi permasalahan yang pelik dalam bidang pengenalan pola. Metode machine learning dapat digunakan untuk mengenali kelas batik melalui pengenalan citra batik. Namun belum banyak penelitian terkait studi komparasi klasifikasi citra batik. Sehingga penelitian ini berfokus pada data set citra batik tulis yang menggunakan dua motif yaitu motif klasik dan motif kontemporer. Pada penelitian ini, fitur ekstraksi menjadi dasar klasifikasi dengan metode Backpropagation Neural Network dan k-Nearest Neighbor. Tujuan dari penelitian ini untuk menemukan pola baru dalam data dengan menghubungkan pola data yang sudah ada dengan data yang baru. Selanjutnya, penelitian ini melakukan perbandingan metode klasifikasi antara Backpropagation Neural Network dan k-Nearest Neighbor untuk mencari metode klasifikasi terbaik untuk klasifikasi Batik tulis Bakaran. Hasil dari studi komparasi menunjukkan bahwa metode Backpropagation Neural Network memperoleh nilai akurasi 90,11% sedangkan metode k-Nearest Neighbor mendapatkan nilai akurasi 96,00%. Sehingga dapat di simpulkan bahwa metode k-Nearest Neighbor merupakan metode terbaik untuk klasifikasi citra batik.
Smart Buildings menggunakan Hyperledger Fabric Blockchain untuk Manajemen Transaksi dan Pemodelan 3D Asmiatun, Siti; Novita Putri, Astrid; Zaman, Badroe
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.751

Abstract

Home construction or home renovation must consider many complex factors. That is because there will be errors / human errors that occur. The impact of that will cause losses to architectural services and eliminate dissatisfied customer trust. Another problem is that customers use intermediaries or third parties in the home construction/renovation process, increasing funds. That is because the process of building/renovating the house is different fromations. This research utilizes a hyper ledger fabric blockchain and intelligent building technology to manage architect and consumer management without intermediaries. Technology can manage home construction/renovation through information on 3-dimensional house plans, initial home budgets, prices for building materials, and daily material needs. The purpose is to monitor the building/renovation of a house without a third party. This study uses the Multimedia Development Life Cycle (MDLC) Development Method to make its application. Meanwhile, blockchain technology is applied using Hyperledger Fabric Software. This research can increase trust and benefit both the customer and the developer. The results of this study are that by making block numbers 65 – 66, it is recorded that each transaction has a processing time from 2022-08-14 02:32:47 to 2022-08-14 02:32:50; it takes approximately.
Analisa Kompetensi Lulusan Sarjana Komputer Melalui Situs Lowongan Kerja Menggunakan Metode SMART Rachmawati, Eka Putri; Hidayati, Nurtriana; Zaman, Badroe
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.413

Abstract

Meeting workforce needs goes beyond producing a certain number of graduates; it requires computer science graduates possessing essential competencies sought by companies. The government supports competency development through Regulation Number 59 of 2018, overseeing the Certificate of Accompanying Diploma (SKPI) and Competency Certificates. Students are encouraged to participate in extracurricular activities to enhance job-relevant competencies. This study, utilizing the Simple Multi Attribute Rating Technique (SMART), assesses the competencies in demand for computer science graduates via popular job vacancy websites. Data from JobStreet, LinkedIn, and Karir reveal the most sought-after competencies, considering keyword searches, Final Project concentrations at Semarang University's Information Technology Department, and alignment with Ministry of Manpower projections in the Information and Communication Technology (ICT) sector. Job vacancies congruent with these projections receive scores. The SMART method results show that (Software) Developer is the most prominently sought competency on job vacancy websites.
Representasi Model 3D Artefak Menggunakan Metode Structure From Motion Berbasis Fotogrametri Di Museum Ranggawarsita Semarang zaman, Badroe; Rachmawati, Eka Putri; Asmiatun, Siti
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): 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.v13i5.4152

Abstract

The development of the digital creative industry is growing very rapidly, utilizing information technology as an option, one of which is three-dimensional (3D) representation technology. 3D representation technology drives innovation in various sectors such as virtual reality, filmmaking, video games, geospatial surveying, architecture, and archaeology. 3D modeling of historical objects has gained attention in recent years. 3D model representation of artifacts is an attempt at digital documentation in case the object is damaged. One of the methods in 3D model representation is by using the photogrammetry-based Structure from Motion (SfM) method. The photogrammetry-based Structure from Motion (SfM) method is a stage of creating 3D models from several artifact images captured by a smartphone camera and then processed into 3D objects from a software. Objects in the form of photos in this study were taken directly from several shooting viewpoints in the Ranggawarsita museum. The results of the 3D model in this study can be utilized as digital assets for various purposes such as educational media for historical objects.
Analisis Klasifikasi Sentimen Neobank: Perbandingan Konfigurasi N-Gram pada TF-IDF Menggunakan Naive Bayes dan SVM Fatha Amin Mujtahid; Badroe Zaman; Galet Guntoro Aji
TIN: Terapan Informatika Nusantara Vol 6 No 12 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i12.9702

Abstract

The increasing number of Neobank users in Indonesia has led to a growth in user reviews on the Google Play Store, which can be utilized to assess service satisfaction and user experience. Manual analysis of these reviews is inefficient, prompting the use of automated machine learning approaches. This study evaluates the effect of N-Gram configurations in TF-IDF feature extraction on the performance of sentiment classification of Neobank reviews using Naive Bayes (NB) and Support Vector Machine (SVM). The dataset consists of 3,798 reviews, preprocessed from 5,000 initial entries collected from Google Play Store Indonesia, with 2,385 positive and 1,413 negative reviews labeled based on star ratings. Data were split using stratified five-fold cross-validation to ensure balanced class proportions in each fold. Features were extracted with TF-IDF using three N-Gram configurations: unigram, bigram, and unigram+bigram. Results indicate that N-Gram configuration significantly affects the performance of both models. NB achieved the highest accuracy with unigram (87.65%), while SVM performed best with unigram+bigram (88.61% accuracy and 88.22% F1-score). Bigram consistently yielded the lowest performance due to short and informal reviews producing sparser features. This study concludes that N-Gram selection should align with algorithm characteristics, and SVM with unigram+bigram is the most effective approach for sentiment classification of Neobank reviews in Indonesia.
Rancang Bangun Sistem Informasi Pemesanan Air Galon Berbasis Web Menggunakan Metode RAD Muhammad Rizal; Galet Guntoro Setiaji; Badroe Zaman
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10069

Abstract

The utilization of digital technology has become an essential aspect of improving business management effectiveness, including in the water refill depot sector. At Air Tirta Kencana Depot in Demak Regency, the ordering process and transaction recording are still carried out manually, resulting in slower data processing, an increased risk of recording errors, and customer service that has not been performed optimally. This study aimed to develop a website-based water gallon ordering system by applying the Rapid Application Development (RAD) approach. Research data were collected through observation, interviews, and literature studies. System testing was conducted involving 13 respondents to evaluate system functionality and usability using Black Box Testing and User Acceptance Testing. The developed system was equipped with integrated features for customer data management, transaction ordering, and sales information within a single platform. The integration of these features facilitated ordering, recording, and transaction monitoring processes in a more organized manner according to the operational needs of the depot. In addition, the system enabled users to access order information more quickly and systematically. Based on the testing results, the web-based information system was able to support transaction recording processes to become faster, more organized, and easier to manage. Furthermore, the system achieved a testing success rate of 80% and assisted in monitoring orders and transactions more accurately. The contribution of this research lies in the development of an integrated ordering and transaction management system within a single platform, enabling depot operational processes to be monitored in a more structured manner. Based on these findings, the system also supports more effective business decision-making and helps improve the overall operational quality of the water depot.
IMPLEMENTASI ALGORITMA KONDISIONAL UNTUK PENANGANAN PEMBAYARAN PARSIAL PADA SISTEM MONITORING KEUANGAN PROYEK KONSTRUKSI Aminuddin Shofi Ashari; Badroe Zaman
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 2 (2026): JATI Vol. 10 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i2.17903

Abstract

Monitoring keuangan pada proyek konstruksi memegang peranan vital dalam menjaga stabilitas arus kas kontraktor. Namun, proses pemantauan pembayaran termin seringkali terkendala oleh kesalahan pencatatan manual, ketidaksesuaian data laporan, dan kesulitan dalam melacak status piutang, terutama ketika terjadi pembayaran secara parsial yang tidak sesuai dengan nilai tagihan awal. Sistem pencatatan konvensional sering kali gagal menangani kondisi dinamis ini secara otomatis. Penelitian ini bertujuan membangun sistem monitoring keuangan proyek konstruksi berbasis web yang mengimplementasikan algoritma kondisional untuk menangani pembayaran parsial (auto-split termin) secara otomatis guna meningkatkan akurasi data. Pengembangan sistem menggunakan metode Waterfall yang meliputi tahapan analisis, desain, implementasi, dan pengujian. Sistem dibangun menggunakan framework PHP Laravel, sedangkan validasi fungsionalitas diuji menggunakan metode Black Box Testing. Algoritma kondisional diterapkan untuk mengevaluasi input pembayaran dan memecah tagihan sisa secara otomatis. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu memvalidasi dan memproses berbagai skenario pembayaran dengan akurat. Fitur auto-split terbukti berhasil menangani pembayaran parsial dengan memisahkan termin menjadi status "terbayar" dan "sisa tagihan" tanpa intervensi manual. Berdasarkan pengujian Black Box, seluruh fungsi berjalan sesuai skenario yang diharapkan, sehingga sistem ini efektif meminimalisir risiko kesalahan administrasi dan mempercepat penyajian informasi arus kas proyek.
PEMANFAATAN GOOGLE FORMS SEBAGAI APLIKASI PEMANTAUAN PEMBERANTASAN JENTIK NYAMUK DI KELURAHAN METESEH KOTA SEMARANG Surono; Badroe Zaman; Krida Pandu Gunata
Jurnal DIMASTIK Vol. 3 No. 2 (2025): Juli
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/dimastik.v3i2.11038

Abstract

Salah satu persoalan kesehatan yang cukup besar di kota Semarang adalah penyakit DBD, berdasarkan data yang dirilis DINKES kota Semarang sampai bulan Juni 2024 yaitu sebesar 237 kasus. Melalui Kader FKK (Forum Kesehatan Keluarga) di Kelurahan, Pemerintah kota Semarang berupaya menekan kasus DBD dengan program Pembarantasan Jentik Nyamuk (PJN). Dalam pelaksanaannya, sering kali ditemukan kendala dalam pengumpulan dan analisis data lapangan yang dapat menghambat efektivitas program. Para Kader FKK juga belum memiliki kemampuan yang cukup untuk memanfaatkan teknologi. Salah satu solusi untuk meningkatkan pemantauan adalah dengan memanfaatkan teknologi informasi. Metode yang digunakan dalam kegiatan ini dalam bentuk ceramah dan praktek mengenai penggunaan Google Forms melalui ponsel pintar. Evaluasi kegiatan juga dilakukan berupa kuisioner yang diisi peserta terkait dengan kegiatan yang telah diikuti. Hasil yang dicapai dari kegiatan ini adalah peningkatan kemampuan mitra dalam membuat aplikasi pemantauan PJN menggunakan Google Forms. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberikan solusi teknologi sederhana namun efektif dalam mendukung program kesehatan lingkungan.
Prediksi Kepuasan Mahasiswa Terhadap Pelayanan Akademik Menggunakan Model Decision Tree Badroe Zaman; lenny margaretta huizen; Muhammad Basyier Ardima
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.8214

Abstract

Perguruan Tinggi merupakan sebuah lembaga pendidikan dimana didalamnya mempunyai tugas dalam pelayanan akademik. Kepuasan mahasiswa dalam memperoleh pelayanan akademik  merupakan hal yang sangat penting dalam menilai sebuah Perguruan Tinggi. Tujuan dari penelitian ini adalah agar dapat mengetahui bagaimana tingkat kepuasan mahasiswa program studi Teknik Informatika dalam hal memperoleh pengajaran oleh dosen, mengenai sarana dan prasarananya. Metode klasifikasi dan prediksi yang digunakan pada penelitian ini diambil dari salah satu model Decision Tree yaitu algoritma C4.5. Algoritma C4.5 berfungsi untuk mengekspolari data, menemukan hubungan tersembunyi antara sejumlah calon variabel input dengan sebuah variabel target. Hasil pengukuran yang didapat adalah nilai akurasi sebesar 94,23%. Nilai recall dari setiap kelas sebesar 94,12% untuk kelas Ya dan 100% untuk kelas Tidak. Sedangkan nilai presisi setiap kelas adalah sebesar 100% untuk kelas Ya dan 25% untuk kelas Tidak.
Klasifikasi Citra Batik Menggunakan Co-Occurrence Matrices Berbasis Wavelet Filter BADROE ZAMAN; Khoirudin Khoirudin
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 5 No. 2 (2021): November (2021)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v17i2.4594

Abstract

Batik is the result of cultural arts that contains a philosophical meaning in each of its motifs. Various types of batik motifs create complexity in the recognition of batik image patterns. Classification of images into certain classes is also a problem in the field of pattern recognition. Machine learning is a method that is very developed at this time. Machine learning method is used to identify batik motifs through batik image classification. This study focuses on the image dataset of written batik which has two motifs, namely classical motifs and contemporary motifs. This study shows the experimental results of batik image classification using the Backpropagation Neural Network, Support Vector Machine and k-Nearest Neighbor classification methods. Co-occurrence matrices as wavelet filter-based feature extraction are used for input into batik image classification. The experimental results show that k-NN gets the best accuracy value of 95.56% while BPNN gets an accuracy value of 85.40% and SVM gets an accuracy value of 76.51%. Based on these results, it can be concluded that k-NN is the best method for classifying batik images with co-occurrence matrices as wavelet filter-based feature extraction.
Co-Authors Ahmad Rifa'i Ahmad Rifa’i Aminuddin Shofi Ashari Astrid Novita Putri, Astrid Eka Putri Rachmawati Fatha Amin Mujtahid Galet Guntoro Aji Handayani, Sri Khoirudin Khoirudin Krida Pandu Gunata Lenny Margaretta Huizen Mohammad Burhan Hanif Muhammad Basyier Ardima Muhammad Rizal Nurtriana Hidayati Prind Triajeng Pungkasanti, Prind Triajeng Prind Triajeng Pungksanti Setiaji, Galet Guntoro Siti Asmiatun, Siti Soiful Hadi Surono Titis Handayani Wicaksana, Dinar Anggit