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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Komputasi Jurnal Sains dan Informatika Sebatik Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal of Electronics and Communications Systems Journal of Data Science and Software Engineering Journal of Embedded Systems, Security and Intelligent Systems Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Komputasi
Andi Farmadi
Universitas Lambung Mangkurat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology Mathematics

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Sistem Pengambilan Keputusan Penentuan Prioritas Pekerjaan Smart City Menggunakan Metode Analytic Hierarchy Process (AHP), Studi Kasus Kabupaten Kotabaru Farmadi, Andi; Ridwan, Ichsan; Sabur, Baharuddin; Hidayat, Rachmat; Srihardjanti, Rurien
Jurnal Sains dan Informatika Vol. 11 No. 2 (2025): 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.v11i2.1870

Abstract

Transformasi digital melalui pengembangan Smart City menjadi salah satu strategi utama dalam meningkatkan efisiensi tata kelola, kualitas pelayanan publik, dan daya saing daerah. Kabupaten Kotabaru telah menyusun Masterplan Smart City dengan berbagai program strategis. Namun, keterbatasan sumber daya menuntut adanya penentuan prioritas pekerjaan yang sistematis. Penelitian ini bertujuan menentukan urutan prioritas pekerjaan berdasarkan metode Analytic Hierarchy Process (AHP). Tiga kriteria utama yang digunakan adalah urgensi, dampak strategis, dan ketergantungan antar kegiatan. Diperoleh Nilai Prioritas AHP yaitu tinggi (AHP > 0.11), sedang (0.09 ≤ AHP ≤ 0.11) dan rendah (AHP < 0.09). Nilai hasil uji consistency ratio (CR) < 0,1 menunjukkan hasil yang dapat diterima dan reliabel. Berdasarkan hasil AHP, pembentukan kelembagaan Smart City menempati prioritas tertinggi, diikuti finalisasi SOP dan e-Gov. Hasil penelitian ini diharapkan dapat dijadikan acuan kuantitatif dalam pengambilan keputusan bagi pemerintah daerah untuk mengoptimalkan alokasi sumber daya dan memastikan inisiatif Smart City dapat dijalankan secara efektif.
Early Fusion of CNN Features for Multimodal Biometric Authentication from ECG and Fingerprint Using MLP, LSTM, GCN, and GAT Priyatama, Muhammad Abdhi; Nugrahadi, Dodon Turianto; Budiman, Irwan; Farmadi, Andi; Faisal, Mohammad Reza; Purnama, Bedy; Adi, Puput Dani Prasetyo; Ngo, Luu Duc
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5299

Abstract

Traditional authentication methods such as PINs and passwords remain vulnerable to theft and hacking, demanding more secure alternatives. Biometric approaches address these weaknesses, yet unimodal systems like fingerprints or facial recognition are still prone to spoofing and environmental disturbances. This study aims to enhance biometric reliability through a multimodal framework integrating electrocardiogram (ECG) signals and fingerprint images. Fingerprint features were extracted using three deep convolutional networks—VGG16, ResNet50, and DenseNet121—while ECG signals were segmented around the first R-peak to produce feature vectors of varying dimensions. Both modalities were fused at the feature level using early fusion and classified with four deep learning algorithms: Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Graph Convolutional Network (GCN), and Graph Attention Network (GAT). Experimental results demonstrated that the combination of VGG16 + LSTM and ResNet50 + LSTM achieved the highest identification accuracy of 98.75 %, while DenseNet121 + MLP yielded comparable performance. MLP and LSTM consistently outperformed GCN and GAT, confirming the suitability of sequential and feed-forward models for fused feature embeddings. By employing R-peak-based ECG segmentation and CNN-driven fingerprint features, the proposed system significantly improves classification stability and robustness. This multimodal biometric design strengthens protection against spoofing and impersonation, providing a scalable and secure authentication solution for high-security applications such as digital payments, healthcare, and IoT devices.
Dynamic Decay Adjustment in Radial Basis Function Networks: Does It Improve Software Defect Prediction? Kamil, Hawariul; Faisal, Mohammad Reza; Farmadi, Andi; Hertono, Rudy; Saputro, Setyo Wahyu
International Journal of Electronics and Communications Systems Vol. 5 No. 2 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i2.29288

Abstract

Software quality depends heavily on the early detection of potentially defective modules, yet the complexity of software metrics and class imbalance often leads to inconsistent prediction performance. This study aims to compare the effectiveness of Radial Basis Function Neural Network (RBFNN) and RBFNN with Dynamic Decay Adjustment (RBFNN-DDA) in predicting software defects using five NASA PROMISE datasets (CM1, KC1, MC1, MW1, and PC1). The research employed quantitative experimentation through data normalization, a 70 to 30 train–test split, and model evaluation across maximum iterations ranging from 200 to 1,000. Model performance was assessed using Accuracy, Precision, Recall, F1 Score, and AUC. The results indicate that RBFNN provides higher Recall and F1 Score, making it better at identifying defective modules, although its performance is less stable. Meanwhile, RBFNN-DDA yields more consistent performance with higher Precision, Accuracy, and AUC on imbalanced datasets, albeit with lower Recall. Both models reached performance saturation at 200 until 400 iterations, showing minimal improvement at higher iteration counts. The findings imply the need for balancing sensitivity and stability when selecting defect prediction models, particularly in environments with severe class imbalance
Comparison Between K-Fold Cross Validation And Percentage Split In Decision Tree Algorithms For Anemia Classification Rahmawati, Nanda Putri; Irwan Budiman; Muhammad Itqan Mazdadi; Andi Farmadi; Friska Abadi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.315

Abstract

Anemia is a significant global health challenge characterized by a pathological deficit in hemoglobin concentration, often leading to physiological instability. Accurate clinical diagnosis typically relies on complete blood count (CBC) tests, which provide critical hematological parameters for classification. While machine learning models have demonstrated high efficacy in diagnosing anemia, existing research often relies on static data partitioning strategies that may overlook evaluation reliability and performance stability. This study addresses this gap by shifting the focus from architectural benchmarking to validation robustness, specifically evaluating the C4.5 algorithm's performance across different data-splitting techniques. The research uses a dataset comprising 1,281 clinical records with 14 numerical features and 9 anemia-type labels. To assess stability, two distinct partitioning strategies were implemented: a static Percentage Split (ranging from 60:40 to 90:10) and iterative K-Fold Cross Validation (with K values of 3, 5, 7, 10, and 15). Experimental results demonstrate that the C4.5 algorithm achieved its peak performance with the 90:10 Percentage Split, achieving an average accuracy of 99.46%, precision of 98.32%, and recall of 99.28%. In comparison, the K-Fold (K=10) approach yielded a slightly lower but more stable accuracy of 99.19% with a significantly reduced standard deviation (±0.09), highlighting its reliability for clinical applications. While the high-ratio percentage split maximizes training exposure and predictive potential, the K-Fold method provides a more objective, generalizable benchmark by accounting for the entire data distribution. The study further identifies challenges in classifying minority classes, such as Leukemia with thrombocytopenia, due to inherent data scarcity. Ultimately, this research confirms that the C4.5 algorithm, when paired with an optimal partitioning protocol, remains a robust and highly interpretable solution for clinical anemia screening, outperforming several complex modern architectures
Iplementasi Fuzzy Pada Monitoring dan Kontrol Kualitas Air Tangki Pembibitan ikan Menggunakan LabView Andi Farmadi; Dwi Kartini; Muliadi Muliadi
Jurnal Komputasi Vol. 9 No. 2 (2021)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v9i2.2864

Abstract

Abstract — Pada kolam pembibitan ikan, pengamatan kualitas air harus terus dilakukan secara berkala karena kondisi pembibitan ikan merupakan masa rawan kematian yang diakibatkan oleh perubahan kondisi lingkungan pembibitan, parameter yang paling berpengaruh dalam kelangsungan hidup ikan yaitu kondisi keasaman air (Ph), kekeruhan air (Turbidiy), oksigen terlarut dalam air (DO) dan suhu air. Parameter tersebut harus selalu dimonitor dan dikontrol untuk mencapai kestabilan lingkungan pembibitan sesuai yang diharapkan. Telah dibuat sistem monitoring dan kontrol terhadap parameter yang berpengaruh pada pembibitan ikan menggunakan sistem fuzzy inferensi. Pengukuran parameter lingkungan dilakukan menggunakan sensor kemudian nilai parameter tersebut disesuaikan dengan nilai fuzzifikasi yang telah dibuat hingga menghasilkan nilai defuzzifikasi, output dari defuzzyfikasi akan melakukan kontrol terhadap parameter tersebut untuk mencapai nilai kestabilan lingkungan air. Pengontrolan Ph dan kekeruhan air dilakukan dengan mengganti air hingga mencapai tinggkat ph dan kejernian air yang sesuai kondisi yang diharapkan, jumlah buangan air dapat dihitung menggunakan teorema fluida. Perhitungan fuzzy dan Pengembangan antarmuka monitoring dan kontrol dibangun menggunakan program berbasis grafik LabView.
Signature Identification Menggunakan Metode Template Matching dan Fuzzy K-Nearest Neighbor Andi Farmadi; Ahmad Faris Asy’arie; 3Irwan Budiman; Dwi Kartini; Ahmad Rusadi Arrahimi; muliadi muliadi
Jurnal Komputasi Vol. 9 No. 1 (2021)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v9i1.2776

Abstract

Abstract — Signature is the result of the process of writing a person of a particular nature as a symbolic substance, which means a symbol or mark. Signature is usually used as an identifying mark of a person, each person must have his own signature in a different pattern. Because it's used as a person's identifying badge, Signatures now become particularly susceptible to counterfeiting and abuse that require check with a signature pattern recognition. This research has created a signature pattern recognition system using methods Template Matching and Fuzzy K-Nearest Neighbor to help recognize a person's signature pattern. The number of signatures used is 110 in two categories: the original signature with 100 data and the false signature with 10 data, and there were 10 classes taken using smartphone cameras. From this research, it was found that the best value from the image size of 200x200 pixels was 92% of the class that owned the signature legible, Positive Predictive Value (PPV) 88% and False Rejection Rate (FRR) 12%, with a k=3 on the original signature, and 90% of the class that owned the signature legible, Negative Predictive Value (NPV) 90% dan False Acceptance Rate (FAR) 10% with a k=9 on the false signature. From these results, it could be concluded that methods Template Matching and Fuzzy K-Nearest Neighbor could be used for signature pattern recognition.Keywords: Pattern, Signature, Template Matching, Fuzzy K-Nearest Neighbor
Perbandingan Nilai K pada Klasifikasi Pneumonia Anak Balita Menggunakan K-Nearest Neighbor Dwi Kartini; Andi Farmadi; Muliadi muliadi; Dodon Turianto Nugrahadi; Pirjatullah Pirjatullah
Jurnal Komputasi Vol. 10 No. 1 (2022)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i1.2965

Abstract

Pneumonia adalah penyakit menular yang menyerang saluran pernapasan bagian bawah dan merupakan salah satu penyebab utama kematian pada anak-anak di bawah lima tahun. Pneumonia mudah menyerang balita yang disebabkan oleh berbagai mikroorganisme yang ada di lingkungan seperti virus, bakteri, jamur dan bakteri mikro. Penelitian ini menggunakan K-Nearest Neighbor (KNN) untuk klasifikasi pneumonia pada pasien berdasarkan gejala yang dialami. Metode klasifikasi KNN dilakukan dengan membandingkan jarak objek antara data tes dan objek keseluruhan pada data pelatihan berdasarkan data riwayat medis pasien. Perbandingan persentase data pelatihan dan data pengujian yang digunakan adalah 90:10, 80:20, dan 70:30 untuk menghitung nilai jarak terdekat dari data pengujian dengan data pelatihan keseluruhan dengan jumlah k yang digunakan. Matriks kebingungan digunakan untuk mengukur hasil tes klasifikasi Pneumonia untuk balita dengan kombinasi jumlah data pelatihan dan data pengujian pada jumlah nilai k = {1, 3, 5, 7, 9, 11}, akurasi tertinggi, presisi, penarikan, dan nilai ukuran-F diperoleh. 0,86, 0,89, 1, dan 0,91 untuk data pelatihan 90%, 10% data pengujian dengan nilai k = 3.
DETEKSI PENYAKIT TANAMAN PADI MENGGUNAKAN EKSTRAKSI FIRUR LBP DAN KLASIFIKASI MODIFIED KNN Andi Farmadi; Muliadi Muliadi
Jurnal Komputasi Vol. 11 No. 2 (2023)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v11i2.13238

Abstract

Daun dan batang padi merupakan bagian utama dalam pemantauan investigasi tanaman padi yang memberikan informasi mengenai status kesehatan tanaman yang mempengaruhi kualitas dan kuantitas hasil tanaman padi. Pemantauan melalaui hasil digitasi daun dan batang dapat mengklasifikasikan penyakit tanaman padi sebagai jenis kelas penyakit berdasarkan data yang diperoleh dari repositori basis data citra pertanian. Data penyakit pada yang digunakan sebanyak 300 data dengan 3 kelas penyakit, yaitu Brown Spot, Hispa, dan Leaf Blast. Digunakan metode analisis tekstur gambar (citra) dengan menggunakan model statistik serta structural, dengan memakai 8 piksel ketetanggan dari sebuah piksel tengah yang dipergunakan dalam operator dasar dari metode Local Binary Pattern (LBP) yang mempunyai ukuran 3x3. Nilai piksel ketetanggaan tersebut dikonversi ke dalam nilai decimal untuk menggantikan nilai piksel tengah. Tahapan pembagian data menggunakan 5-Fold Cross validation. Metode Modified K-Nearest Neighbor digunakan untuk melakukan pengklasifikasian untuk identifikasi terhadap citra daun Padi. Dimana pada tahap klasifikasi data di uji secara manual satu-persatu pada saat proses klasifikasi. dari tiga kelas dan masing-masing memiliki 100 data, totalnya ada 300 data. Dalam 5 cross-validation. Hasil uji didapatkan model klasifikasi dengan nilai akurasi tertinggi sebesar 81,24%, pada K=13.
Evaluating CNN Robustness for Face Mask Classification under Environmental Variations Bagaskara Ridho Vandio; Fatma Indriani; Andi Farmadi; Dodon Turianto Nugrahadi; Friska Abadi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i2.2617

Abstract

Purpose – This study aims to analyze and compare the performance of ResNet50 and MobileNetV3 for multi-class face mask classification under various environmental conditions. Design/methods/approach – ResNet50 and MobileNetV3 are trained using transfer learning for three-class face mask classification and evaluated under normal conditions and environmental variations, including illumination changes, blur, low compression, and rotation. Findings – Experimental results show that ResNet50 achieves an accuracy of 94.32% under normal conditions, slightly outperforming MobileNetV3 at 94.10%. Under environmental variations, the largest performance degradation is observed under darkening and blur conditions, while low compression and rotation have relatively minor effects. ResNet50 demonstrates higher robustness across most perturbation settings, whereas MobileNetV3 provides competitive performance with substantially better computational efficiency. Research implications/limitations – This study is limited to a controlled evaluation using synthetic environmental perturbations on a single dataset and does not consider broader dataset diversity. Therefore, the findings should be interpreted within the evaluated experimental conditions. Originality/value – This study provides a comparative analysis of model robustness under controlled environmental perturbations, highlighting the trade-off between robustness and computational efficiency for face mask classification systems.
Co-Authors 3Irwan Budiman Abdilah, Muhammad Fariz Fata Abdullayev, Vugar Achmad Rizal Adi, Puput Dani Prasetyo Ahdyani, Annisa Salsabila Ahmad Bahroini Ahmad Faris Asy’arie Ahmad Faris Asy’arie Ahmad Juhdi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Tajali Akhmad Yusuf Ando Hamonangan Saragih Ardiansyah Sukma Wijaya Arif, Nuuruddin Hamid Arifin Hidayat Aris Pratama Azizah, Siti Roziana Bachtiar, Adam Mukharil Bagaskara Ridho Vandio Bahriddin Abapihi Bedy Purnama Deni Sutaji Dita Amara Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Dzira Naufia Jawza Efendi Mohtar Erdi, Muhammad Evi Nadya Prisilla Faisal Murtadho Fathul Hadi Fatma Indriani Fitria Agustina fitria Friska Abadi Ghinaya, Helma Gita Malinda Hertono, Rudy Heru Candra Kartika Heru Kartika Chandra I Gusti Ngurah Antaryama Ichsan Ridwan Irwan Budiman Irwan Budiman Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Kamil, Hawariul Keswani, Ryan Rhiveldi Khairunnisa Khairunnisa Lisnawati M. Apriannur Miftahul Muhaemen Muhammad Alkaff Muhammad Halim Muhammad Itqan Mazdadi Muhammad Khairin Nahwan Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muhammad Ridha Maulidi Muhammad Rusli Muliadi Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi muliadi muliadi Musyaffa, Muhammad Hafizh Mutiara Ayu Banjarsari Nafis Satul Khasanah Ngo, Luu Duc Noryasminda Nugraha, Muhammad Amir Nugrahadi, Dodon Nurcahyati, Ica Nurlatifah Amini P., Chandrasekaran Patrick Ringkuangan Pirjatullah Pirjatullah Pirjatullah Priyatama, Muhammad Abdhi RACHMAT HIDAYAT Radityo Adi Nugroho Rahmawati, Nanda Putri Raidra Zeniananto Ramadhan, As`'ary Rifki Izdihar Oktvian Abas Pullah Rifki Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rusdiani, Husna Sabur, Baharuddin Salsabila Anjani Saragih, Triando Hamonangan Sa’diah, Halimatus Setyo Wahyu Saputro Shalehah Srihardjanti, Rurien Suci Permata Sari Syahputra, Muhammad Reza Tajali, Ahmad Ulya, Azizatul Umar Ali Ahmad Wijaya Kusuma, Arizha Winda Agustina YILDIZ, Oktay