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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL INSTEK (Informatika Sains dan Teknologi) Prosiding SNITP (Seminar Nasional Inovasi Teknologi Penerbangan) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika bit-Tech Jurnal Diferensial Enthusiastic : International Journal of Applied Statistics and Data Science Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Komputer Indonesia INOVTEK Polbeng - Seri Informatika Prosiding Seminar Nasional Ilmu Teknik
Alfan Rizaldy Pratama
Universitas Pembangunan Nasional Veteran Jawa Timur
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Public Health Transportation

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Classification Of Brain Tumors Using The VGG19 Method Syah, Maulidya Prastita; Kristanaya, Mirechelin; Nariswari, Naura Ulayya; Azzahra, Melinda Putri; Pratama, Alfan Rizaldy; Saputra, Wahyu S.J.
Jurnal Komputer Indonesia Vol. 3 No. 2 (2024): Desember
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jki.v3i2.677

Abstract

Brain tumor is one of the diseases that has a high mortality rate and requires early detection to increase the chance of cure. In recent years, artificial intelligence-based methods, especially Deep Learning, have shown promising performance in brain tumor classification using Magnetic Resonance Imaging (MRI) images. This study applies the VGG19 architecture, one of the Convolutional Neural Network (CNN) models, to classify brain tumor types based on MRI images. The model is trained with data that has gone through augmentation and contrast enhancement processes to improve image quality before classification. The experimental results show that the VGG19 method is able to achieve high accuracy in brain tumor classification. These findings confirm the effectiveness of VGG19 in automatically detecting brain tumors and can be a supporting solution for medical personnel in performing early diagnosis.
Underwater Single and Multiple Objects Detection Based on the Combination of YOLOv7-tiny and Visual Feature Enhancement Sari, Dewi Mutiara; Marta, Bayu Sandi; Dwito Armono, R. Haryo; Rizaldy Pratama, Alfan; Putra Pratama, Firmansyah
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/91b9qn06

Abstract

Breakwater construction in Indonesia frequently employs tetrapods to dissipate wave energy. However, the placement process remains manual, relying on divers to guide crane operators. This approach not only poses safety risks but also limits visibility due to underwater turbidity. While prior research has focused on underwater image enhancement, the integration of tetrapod object detection remains unexplored. This study proposes a combined method of underwater image enhancement and tetrapod object detection to support land-based operator visualization. Auto-level filtering and histogram equalization techniques were applied to enhance image clarity, followed by object detection using the YOLOv7-tiny model. Tetrapod models at a 1:20 scale were used for training and testing. The proposed system achieved a mean average precision (mAP) of 0.95. Evaluation was conducted across 12 scenarios, involving four lighting levels and two water conditions: clear and 45.8% turbidity. The object detection confidence scores were 0.80 without enhancement, 0.85 with histogram equalization, and 0.84 with auto-level filtering. Multiple object detection achieved an accuracy of 88.75%, outperforming previous approaches using YOLOv4-tiny. The results demonstrate the potential of integrating image enhancement and deep learning-based object detection for improving underwater operational safety and placement precision in breakwater construction.
Evaluasi Kinerja Uji Normalitas pada Ragam Distribusi dan Ukuran Sampel Wara, Shindi Shella May; Adziima, Andri Fauzan; Nasrudin, Muhammad; Pratama, Alfan Rizaldy
JURNAL DIFERENSIAL Vol 7 No 2 (2025): November 2025
Publisher : Program Studi Matematika, Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jd.v7i2.24042

Abstract

The normal distribution is a fundamental assumption in many parametric statistical methods. Therefore, testing for data normality is a crucial step prior to further analysis. This study aims to evaluate the performance of three widely used normality test methods: Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and Shapiro-Wilk (SW), across various distributions (standard normal, exponential, and t-student with degrees of freedom 1, 20, and 100) and sample sizes (n = 20, 50, 100, 200, and 500). Data were generated through simulation with 1000 iterations for each combination. The results show that the KS method performs well on standard normal and t-student distributions with larger degrees of freedom. The AD method proves to be more sensitive, especially in detecting deviations from normality, though it is less stable for small sample sizes. Meanwhile, the SW method demonstrates optimal performance with large samples. These findings provide practical guidance in selecting appropriate normality test methods based on the characteristics of the data.
Optimization of Palm Fruit Ripeness Detection With Yolov11 on CPU Anniswa, Iqbal Ramadhan; JAUHARIS SAPUTRA, Wahyu Syaifullah; Idhom, Mohammad; Rizaldy Pratama, Alfan; Susrama Mas Diyasa, I Gede
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 4 (2025): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.111253

Abstract

The palm oil industry is one of the strategic sectors that contributes significantly to the Indonesian economy. However, this industry still faces various challenges, particularly in terms of operational efficiency and the implementation of digitalization, especially at the level of independent farmers who often still use manual methods to determine the ripeness of the fruit. This manual process is prone to subjectivity, which can impact harvest quality and supply chain efficiency. To address this issue, this study proposes a palm oil fruit ripeness detection system based on the YOLOv11 algorithm, chosen for its advantages in inference speed and detection accuracy, especially when run on devices with limited resources. The developed model was then implemented using the ONNX Runtime Framework. This enables accelerated inference processes and supports portability on hardware with limited resources. Test results show that the model achieves an mAP@50 accuracy of 90.2% with an average latency of around 255 ms to 300 ms. With these achievements, this system is not only reliable in detecting fruit ripeness, but also efficient in processing time and relevant to support digital transformation in the palm oil plantation sector.
Optimalisasi Deteksi Wajah Real-Time Menggunakan HAAR Cascade Classifier berbasis OpenCV Alfan Rizaldy Pratama; Muhammad Nasrudin; Andri Faudzan Adziima; Shindi Shella May Wara
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 1 (2025): Juni 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i1.15485

Abstract

Nowadays, the face is one of the features that is widely used in various aspects of life such as security which includes access control and surveillance, biometrics which includes attendance systems, and many others. The obstacles found in implementing this are generally about speed performance when detecting, this is vital because if the process takes a long time, misconceptions and system errors will occur. HAAR Cascade Classifier is one of the most widely used lightweight face detection algorithms. In this research, by analyzing the use of grayscale color compared to RGB, a performance increase of 6.17% is obtained with an average FPS on RGB of 25.63 while on grayscale it is 27.21.
DEPLOYMENT DETEKSI KEMATANGAN BUAH KELAPA SAWIT BERBASIS YOLOV11 DENGAN ONNX RUNTIME DAN STREMLIT Ramadhan Anniswa, Iqbal; Syaifullah J. S, Wahyu; Idhom, Mohammad; Rizaldy Pratama, Alfan; Gede Susrama Mas Diyasa, I
Prosiding SNITP (Seminar Nasional Inovasi Teknologi Penerbangan) Vol. 9 No. 1 (2025): SNITP 2025
Publisher : Politeknik Penerbangan Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kelapa Sawit merupakan komoditas strategis di Indonesia yang menjadi salah satusumber devisa utama. Tingkat kematangan buah kelapa sawit sangat berpengaruhterhadap kualitas minyak yang dihasilkan, sehingga diperlukan metode yang cepat,tepat, dan konsisten untuk mendeteksi tingkat kematangan buah. Dalam metedoKonversional masih mengandalkan pengamatan visual oleh pekerja lapangan seringbersifat subjektif dan tidak efesien.Dengan hal tersebut,penelitian ini mengusulkanpenerapan model object detection berbasis YOLOv11 untuk mendeteksikematangan buah kelapa sawit. Model YOLOv11 dipilih karena memilikikeunggulan dalam kecepatan inferensi dan akurasi deteksi pada objek kecil maupunkompleks. Untuk memfasilitasi penggunaan di lingkungan produksi,Model yangtelah dilatih dikonversi ke format ONNX dan dijalankan menggunakan ONNXRuntime agar memperoleh perfoma inferensi yang lebih optimal pada sumber dayaterbatas. Selanjutnya, aplikasi antarmuka berbasis Streamlit dikembangkan untukmemudahkan pengguna dalam mengunggah gambar atau video dan memperolehhasil deteksi secara real-time. Diharapkan, sistem ini mampu memberikan solusipraktis, efisien, dan akurat dalam mendukung proses panen buah kelapa sawit.
Analisis Performa Convolutional Neural Network untuk Klasifikasi Aktivitas Pengemudi Terganggu Zufar Abdullah Rabbani; Wahyu Syaifullah J S; Alfan Rizaldy Pratama
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.210

Abstract

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.
CLASSIFICATION OF HUMAN AND AI-GENERATED INDONESIAN POP SONGS BASED ON SPECTROGRAM USING CONVOLUTIONAL NEURAL NETWORK Faris Nur Tsani; Wahyu Syaifullah Jauharis Saputra; Alfan Rizaldy Pratama
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i1.65432

Abstract

Pesatnya perkembangan teknologi kecerdasan buatan (AI) memicu lonjakan produksi lagu generatif yang menyerupai karya manusia, sehingga menghadirkan tantangan signifikan terhadap orisinalitas dan hak cipta musik. Penelitian ini bertujuan mengklasifikasikan lagu Pop Indonesia kategori human-generated dan AI-generated menggunakan pendekatan Convolutional Neural Network (CNN) berbasis arsitektur ResNet-18. Dataset terdiri dari 100 lagu berformat MP3 yang terbagi seimbang antara karya manusia dan karya AI dari platform Suno dan Udio. Data audio diproses melalui teknik segmentasi overlapping window berdurasi 10 detik dengan overlap 5 detik, kemudian diekstraksi menjadi citra spektrogram Short-Time Fourier Transform (STFT). Total data yang dihasilkan mencapai 4.282 segmen audio. Hasil pelatihan selama 100 epoch menunjukkan bahwa model mencapai konvergensi dengan train accuracy 100% dan validation accuracy 95,09%. Pada tahap pengujian menggunakan data yang belum pernah dilihat sebelumnya, model menunjukkan performa unggul dengan tingkat akurasi 93,01%. Temuan ini mengonfirmasi bahwa penggunaan representasi spektogram dalam arsitektur CNN mampu menangkap perbedaan fitur frekuensi dan temporal secara efektif untuk mengidentifikasi musik berbasis AI pada genre Pop Indonesia.
VERIFIKASI OTOMATIS SERTIFIKAT MENGGUNAKAN IMPROVED SIFT DAN RANSAC BERBASIS ANALISIS VISUAL LOGO DAN STEMPEL Maulidya Prastita Syah; Wahyu Syaifullah Jauharis Saputra; Alfan Rizaldy Pratama
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i1.66776

Abstract

Proses verifikasi sertifikat prestasi pada jalur Seleksi Nasional Berdasarkan Prestasi (SNBP) masih dilakukan secara manual dan menghadapi tantangan akibat tingginya jumlah dokumen serta variasi kualitas citra. Kondisi ini meningkatkan risiko kesalahan dalam menilai kredibilitas sertifikat, khususnya pada elemen visual seperti logo dan stempel yang tidak valid. Penelitian ini bertujuan mengembangkan metode otomatis dalam mengidentifikasi kredibilitas sertifikat berbasis analisis fitur visual. Metode yang digunakan adalah Improved Scale Invariant Feature Transform (Improved SIFT) untuk mengekstraksi fitur lokal yang stabil, serta Random Sample Consensus (RANSAC) untuk menyaring kecocokan fitur berdasarkan konsistensi geometris. Pencocokan dilakukan antara citra sertifikat dan citra parameter berupa logo dan stempel yang telah diidentifikasi sebagai tidak valid. Pengujian dilakukan pada 200 citra sertifikat yang terdiri dari dua kelas, yaitu kredibel dan tidak kredibel yang menunjukkan Improved SIFT mampu meningkatkan kualitas representasi fitur dengan akurasi sebesar 0.47 dibandingkan SIFT standar sebesar 0.16. Selanjutnya, RANSAC mampu menyaring false match dengan peningkatan akurasi dari 0.47 menjadi 0.91 setelah RANSAC. Kontribusi penelitian ini adalah mengintegrasikan Improved SIFT dan RANSAC untuk meningkatkan validitas pencocokan fitur visual dalam mendeteksi indikasi ketidak-kredibelan sertifikat pada citra dengan variasi kualitas tinggi. Dengan demikian, metode yang diusulkan mampu meningkatkan keandalan sistem dalam proses verifikasi otomatis berbasis analisis visual.
Sharpe Ratio-Based Dynamic Crypto Asset Allocation with Trend Filtering Using SMA Fauzan Adziima, Andri; Wara, Shindi Shella May; Nasrudin, Muhammad; Pratama, Alfan Rizaldy
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art1

Abstract

This paper proposes a dynamic cryptocurrency asset allocation strategy that combines Sharpe Ratio-based weighting with trend filtering using the Simple Moving Average (SMA) of Bitcoin (BTC). The model reallocates capital among a portfolio of seven major cryptocurrencies (BTC, ETH, BNB, SOL, TON, TRX, XRP) every three days, conditional on BTC trading above its respective SMA threshold (50-day, 100-day, or 200-day). When BTC trends below the SMA, the strategy shifts fully to USDT to minimize downside risk. Using historical data from January 1, 2024, to January 1, 2025, the study evaluates performance across three SMA configurations and benchmarks against a buy-and-hold baseline. Results show that the SMA-50 strategy achieved the highest cumulative return (+231.51%) and Sharpe Ratio (2.51), significantly outperforming both the longer SMA-based models and the baseline average return (+132.14%). Risk analysis indicates that shorter SMA windows allow more responsive exposure during market uptrends but increase short-term volatility. Overall, the findings support the use of hybrid strategies combining trend-following filters and risk-adjusted allocation for managing crypto portfolios in volatile environments.
Co-Authors Abda Abda Adziima, Andri Fauzan Alvino Hadiyan Pradipta Andri Faudzan Adziima Andri Fauzan Adziima Anniswa, Iqbal Ramadhan Azzahra, Melinda Putri Dadet Pramadihanto Dwito Armono, R. Haryo Faris Nur Tsani Fauzan Adziima, Andri Gede Susrama Mas Diyasa, I I Gede Susrama Mas Diyasa idhom, Mohammad Iqbal Ramadhan Anniswa Jauharis Saputra, Wahyu Syaifullah Kristanaya, Mirechelin Maretta Fairuz Luthfia Winoto Putri Marta, Bayu Sandi Maulidya Prastita Syah Mohammad Idhom Muhammad Nasrudin Muhammad Nasrudin Muhammad Rafli Feandika Nugroho Nariswari, Naura Ulayya Putra Pratama, Firmansyah Ramadhan Anniswa, Iqbal Reza Putri Angga Saputra, Wahyu S.J. Sari, Dewi Mutiara Shindi Shella May Wara Susrama Mas Diyasa, I Gede Syah, Maulidya Prastita Syaifullah J. S, Wahyu Wahyu Syaifullah J S Wahyu Syaifullah Jauharis Saputra Wahyu Syaifullah Jauharis Saputra Zufar Abdullah Rabbani