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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Prosiding SNITP (Seminar Nasional Inovasi Teknologi Penerbangan) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Teknologi Informatika dan Komputer Jurnal Diferensial MATHunesa: Jurnal Ilmiah Matematika 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.
Pengembangan Algoritma Sharpe Ratio dengan Integrasi Filter Tren SMA dalam Strategi Portofolio Aset Kripto Adziima, Andri Fauzan; Wara, Shindi Shella May; Nasrudin, Muhammad; Pratama, Alfan Rizaldy
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 1 (2025): Juni 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i1.383

Abstract

Penelitian ini mengusulkan sebuah strategi alokasi aset kripto yang bersifat dinamis, dengan menggabungkan pembobotan berdasarkan Sharpe Ratio dan penyaringan tren menggunakan indikator Simple Moving Average (SMA) dari Bitcoin (BTC). Model ini melakukan alokasi ulang modal setiap tiga hari pada tujuh aset kripto utama (BTC, ETH, BNB, SOL, TON, TRX, XRP), dengan ketentuan bahwa harga BTC berada di atas ambang SMA tertentu (50 hari, 100 hari, atau 200 hari). Apabila BTC berada di bawah nilai SMA tersebut, seluruh portofolio secara otomatis dialihkan ke USDT untuk menekan risiko penurunan nilai. Studi ini menggunakan data historis dari 1 Januari 2024 hingga 1 Januari 2025 dan menguji performa model dalam tiga konfigurasi SMA, lalu dibandingkan dengan strategi dasar buy-and-hold. Hasil menunjukkan bahwa strategi dengan parameter SMA 50 hari menghasilkan return kumulatif tertinggi (+231,51%) serta rasio Sharpe terbaik (2,51), jauh melampaui model dengan SMA yang lebih panjang maupun rata-rata return dari strategi dasar (+132,14%). Analisis risiko mengindikasikan bahwa jendela SMA yang lebih pendek memberikan respons yang lebih cepat terhadap tren naik pasar, meskipun disertai dengan peningkatan volatilitas jangka pendek. Secara keseluruhan, temuan ini menguatkan efektivitas strategi hibrida yang mengombinasikan penyaringan tren dengan alokasi berbasis risiko dalam pengelolaan portofolio kripto di tengah kondisi pasar yang fluktuatif.
Optimasi Sistem Antrian Pada Medical Center ITS Dengan Simulasi Discrete Event Dan Response Surface Methodology Wara, Shindi Shella May; Nasrudin, Muhammad; Adziima, Andri Fauzan; Pratama, Alfan Rizaldy
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 1 (2025): Juni 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i1.411

Abstract

Medical Center ITS berfungsi sebagai unit rawat jalan yang melayani pemeriksaan, tindakan medis, penunjang medis, dan rujukan bagi civitas academica ITS serta masyarakat umum dengan biaya yang terjangkau. Penelitian ini bertujuan untuk mengoptimalkan sistem antrean di Medical Center ITS, yang sering menghadapi antrean panjang dan berdampak pada waktu tunggu pasien serta efisiensi pelayanan, menggunakan pendekatan simulasi diskrit. Data primer, meliputi waktu antar kedatangan dan waktu pelayanan (resepsionis, poli umum, poli gigi, resep, dan pengambilan obat), dikumpulkan secara empiris untuk memodelkan sistem antrean berbasis kejadian. Model simulasi yang dikembangkan secara akurat merepresentasikan seluruh alur pelayanan. Hasil simulasi menunjukkan bahwa sistem pelayanan saat ini belum optimal dengan hanya satu server di poli umum dan satu di poli gigi. Berdasarkan temuan, skenario penambahan server pada poli gigi menjadi empat dan tetap satu server di poli umum diusulkan sebagai konfigurasi optimum. Implementasi skenario ini terbukti secara signifikan mengurangi waktu tunggu rata-rata pasien dan meningkatkan tingkat utilitas sumber daya. Penelitian ini menegaskan bahwa simulasi diskrit adalah alat pengambilan keputusan yang efektif untuk meningkatkan kualitas dan efisiensi pelayanan di fasilitas kesehatan.
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.
Klasterisasi Kabupaten/Kota di Provinsi Jawa Tengah berdasarkan Komponen Indeks Pembangunan Manusia Menggunakan Metode UMAP dan K-MEANS Difta Alzena Sakhi; Friza Nur Fatmala; Karina Auralia; Alfan Rizaldy Pratama; Aviolla Terza Damaliana
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

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

Abstract

Ketimpangan pembangunan manusia di Provinsi Jawa Tengah tetap menjadi tantangan besar yang memerlukan pendekatan berbasis data. Penelitian ini mengelompokkan kabupaten/kota berdasarkan komponen Indeks Pembangunan Manusia (IPM) tahun 2019–2024 menggunakan algoritma K-Means. Variabel yang dianalisis meliputi Usia Harapan Hidup, Harapan Lama Sekolah, Rata-Rata Lama Sekolah, dan Pengeluaran per Kapita. Data diproses melalui tahapan penting, seperti standarisasi, deteksi outlier, dan reduksi dimensi menggunakan Uniform Manifold Approximation and Projection (UMAP), serta penentuan jumlah klaster optimal dengan Elbow Method dan Silhouette Score. Hasil analisis menunjukkan empat klaster optimal dengan nilai Silhouette Score sebesar 0,71, yang mengelompokkan data tahunan seluruh kabupaten/kota ke dalam kelompok-kelompok dengan tingkat pembangunan manusia yang berbeda secara signifikan. Klaster dengan nilai IPM tertinggi terdiri dari kota-kota besar, seperti Semarang, Salatiga, dan Surakarta, sementara klaster terendah mencakup wilayah yang masih menghadapi berbagai kendala dalam aspek kesehatan, pendidikan, dan ekonomi. Visualisasi UMAP membantu interpretasi distribusi klaster dan memberikan masukan strategis bagi kebijakan pembangunan wilayah yang lebih merata. Kata Kunci: klasterisasi, indeks pembangunan manusia, K-Means, UMAP, Jawa Tengah
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.
Co-Authors Abda Abda Adziima, Andri Fauzan Andri Faudzan Adziima Anniswa, Iqbal Ramadhan Aviolla Terza Damaliana Azzahra, Melinda Putri Dadet Pramadihanto Difta Alzena Sakhi Dwito Armono, R. Haryo Friza Nur Fatmala Gede Susrama Mas Diyasa, I idhom, Mohammad Jauharis Saputra, Wahyu Syaifullah Karina Auralia Kristanaya, Mirechelin Marta, Bayu Sandi Muhammad Azkiya Akmal Muhammad Nasrudin Nariswari, Naura Ulayya Putra Pratama, Firmansyah Ramadhan Anniswa, Iqbal Saputra, Wahyu S.J. Sari, Dewi Mutiara Shindi Shella May Wara Susrama Mas Diyasa, I Gede Syah, Maulidya Prastita Syaifullah J. S, Wahyu Trimono Wahyu Syaifullah J S Zufar Abdullah Rabbani