Article Per Year (5 Year)
p-Index From 2020 - 2025
4.818
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA JTEV (Jurnal Teknik Elektro dan Vokasional Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Jurnal Pengabdian Kepada Masyarakat (Indonesian Journal of Community Engagement) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal TIPS : Jurnal Teknologi Informasi dan Komputer Politeknik Sekayu IJISTECH (International Journal Of Information System & Technology) Jurnal Pendidikan dan Konseling Jurnal Berdaya Mandiri Journal of Intelligent Decision Support System (IDSS) JUKI : Jurnal Komputer dan Informatika Suluah Bendang: Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) IJISTECH Jurnal Teknologi dan Sistem Tertanam Parta: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Pengabdian Masyarakat Bhinneka Electrician : Jurnal Rekayasa dan Teknologi Elektro Jurnal Pengabdian Pada Masyarakat Jurnal Relawan dan Pengabdian Masyarakat REDI TeknoKreatif: Jurnal Pengabdian kepada Masyarakat
Afriansyah, Aidil
Informatics Engineering Study Program, Institut Teknologi Sumatera
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 37 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 37 Documents
Search

 1   2   3   4 
Penggunaan Algoritma Gaussian Naïve Bayes & Decision Tree Untuk Klasifikasi Tingkat Kemenangan Pada Game Mobile Legends Yoga Naufal Ray Putro; Aidil Afriansyah; Radhinka Bagaskara
JUKI : Jurnal Komputer dan Informatika Vol. 6 No. 1 (2024): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v6i1.472

Abstract

The development of technology and the internet has increased the popularity of online games, such as Mobile Legends. However, in competitions, players often experience defeat due to various factors, including player skills, team strategies, and the right hero selection. The right hero selection is very important to increase the chances of winning. Therefore, the Mobile Legends Professional League (MPL) has become a focus for competitive teams around the world. This study aimed to determine the classification of victory in MPL matches based on draft pick. Gaussian Naïve Bayes and Decision Tree were used as classification algorithm models in this study. The process in this study included cleaning data, data transformation (labeling), handling imbalanced data, scaling, splitting, and hyperparameter. The evaluation stage used confusion matrix, correlation data, and AU-ROC curve. The results of this study showed that the Decision Tree method had better performance than Gaussian Naïve Bayes in classifying data using the confusion matrix. The AUC (area under the receiver operating characteristic curve) analysis showed that the decision tree had better performance than Gaussian naive Bayes in predicting positive and negative data. This is indicated by the higher AUC value for the Decision Tree, which is 0.67 compared to Gaussian Naïve Bayes which is 0.48. Classification models with higher AUC values can more accurately distinguish between positive and negative data. In this study, the Decision Tree had a higher AUC value than Gaussian Naïve Bayes so the Decision Tree could more accurately classify victory and defeat data.
Rancang Bangun Prototype Budidaya Ikan dalam Ember Berbasis Internet of Things (IoT) Sebagai Media Pembelajaran di Sekolah Global Madani Sabar, Sabar; Afriansyah, Aidil; Pertiwi, Kisna; Chalid, Achmad AA; Mufidah, Zunanik
JTEV (Jurnal Teknik Elektro dan Vokasional) Vol 10, No 2 (2024): JTEV (Jurnal Teknik Elektro dan Vokasional)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtev.v10i2.128195

Abstract

Penelitian ini bertujuan untuk memperkenalkan dan menerapkan teknologi budidaya ikan dalam ember berbasis Internet of Things (IoT) sebagai sistem kontrol instrumentasi dasar. Metode penelitian Prototype ini dengan tahapan berupa perancangan hardware dan software dalam praktik didampingi langsung oleh tim peneliti kepada Guru dan siswa. Pemanfaatkan teknologi IoT, pelatihan ini tidak hanya meningkatkan efisiensi dalam budidaya ikan tetapi juga memberikan pemahaman yang lebih baik tentang sistem kontrol instrumentasi dasar. Hasil analisis menunjukkan peningkatan pengetahuan peserta sebesar 24% dalam bidang budidaya ikan, dari 63% awal menjadi 87%. Tujuan dari penerapan teknologi ini adalah untuk menunjukkan peningkatan wawasan bagi guru dan siswa sekolah Global Madani sebagai media pembalajaran Inovasi teknologi, edukasi budidaya ikan dalam memanfaatkan ruang terbatas dengan solusi inovasi aplikasi teknologi budidaya ikan dalam ember berbasis IoT. Sistem ini dapat memantau suhu air, kelembaban, pH, dan tingkat oksigen dalam ember, serta memberikan informasi secara real-time kepada pengguna melalui internet. Dengan menggunakan IoT, pengguna dapat memastikan bahwa lingkungan budidaya ikan tetap optimal, sehingga mempercepat pertumbuhan ikan dan meningkatkan hasil produksi. Pengguna juga dapat melakukan intervensi secara tepat waktu jika ada masalah dengan lingkungan budidaya, seperti perubahan suhu air atau tingkat oksigen yang rendah dengan monitoring langsung menggunakan smartphone. Pendekatan pembelajaran berbasis pemecahan masalah dan praktik langsung menggunakan prototype dibuktikan keaktipan antusiasme peserta dan hasil praktikum yang efektif sehingga disimpulkan adanya peningkatkan kreativitas mahasiswa.
Optimizing Driving Completeness Prediction Models: A Comparative Study of YOLOv7 and Naive Bayes at Institut Teknologi Sumatera Algifari, Muhammad Habib; Ashari, Ilham Firman; Nugroho, Eko Dwi; Afriansyah, Aidil; Vebriyanto, Mario
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6761

Abstract

The number of vehicles in Indonesia is increasing every year. The number of motor vehicle accidents in 2022 will be more than 100,000. It is hoped that several regulations regarding motorbike rider equipment will increase awareness of rider safety. By utilizing image recognition technology developed with artificial intelligence, it is possible to create digital image processing models or images that are fast and accurate for detecting driving equipment. The object detection model developed uses a dataset in the form of images of motorists who want to enter ITERA through the main gate. The object detection model will also be integrated with the classification model to create a program that can detect motorbike rider equipment, such as mirrors, helmets, not wearing a helmet, shoes, not wearing shoes, open clothes, and closed clothes. After detecting motorized rider equipment in the classification area, the results will be transferred to a classification model to determine the level of safety for motorized riders, either insufficient or sufficient safety. The test results show that the optimal object detection model was found at an epoch value of 70 with a batch-size of 16, producing a mAP value of 0.8914. The optimal classification model uses the naive Bayes method which has been trained with a dataset of 62 data and achieves an accuracy of 94%.
Development of YOLO-Based Mobile Application for Detection of Defect Types in Robusta Coffee Beans Nugroho, Eko Dwi; Verdiana, Miranti; Algifari, Muhammad Habib; Afriansyah, Aidil; Firmansyah, Hafiz Budi; Rizkita, Alya Khairunnisa; Winarta, Richard Arya; Gunawan, David
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8886

Abstract

Improving the quality of Robusta coffee beans is a crucial challenge in the coffee industry to ensure that consumers receive high-quality products. However, the identification of defects in coffee beans is still largely performed manually, making the process error-prone and time-consuming. This study aims to develop a YOLO-based mobile application to detect defects in Robusta coffee beans quickly and accurately. The method employed in this study is YOLO, a deep learning-based object detection algorithm known for its real-time object detection capabilities. The application was tested using a dataset of Robusta coffee beans containing various defects, such as broken, black, and wrinkled beans. The test results indicate that the application achieves high detection accuracy, with the black bean class achieving 95.3% accuracy, while the moldy or bleached bean class records the lowest accuracy at 62.2%. This application is expected to assist farmers and coffee industry stakeholders in improving the quality of Robusta coffee beans and enhancing the efficiency of the sorting process.
The Development and Implementation of M-Edupayment: A Multi-Payment Platform for SMK Negeri 7 Bandar Lampung Praseptiawan, Mugi; Utoro, Meida Cahyo; Ashari, Ilham Firman; Algifari, Muhammad Habib; Afriansyah, Aidil
Jurnal Pengabdian kepada Masyarakat (Indonesian Journal of Community Engagement) Vol 11, No 1 (2025): March
Publisher : Direktorat Pengabdian kepada Masyarakat Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jpkm.100850

Abstract

This community engagement activity aimed to develop a mini bank website, m-EduPayment, integrated with the iPaymu payment gateway as an Application Programming Interface (API). The project was implemented at SMK Negeri 7 Bandar Lampung. System development followed the Personal Extreme Programming (PXP) method, which included the stages of Requirements, Planning, Iteration Initialization, Design, Implementation, and System Testing. PXP, a variant of Extreme Programming, was specifically adapted for individual developers. The activity involved 31 respondents, consisting of 10th and 11th-grade students, who evaluated the system using the System Usability Scale (SUS) to measure its usability. Interviews with teachers (superadmins) and student administrators were also conducted to identify initial requirements and gather feedback on the system design. Data analysis utilized a Likert scale, where respondents rated various system aspects on a scale from 1 (strongly disagree) to 5 (strongly agree). SUS scores were calculated using standard formulas to determine a final score, which was then classified into usability categories. The average SUS score was 91, falling under the "Excellent" category (Grade A). The development of the mini bank website introduced new features, including online payment services for tuition fees (SPP), waste banks, and savings. System testing achieved a 99% functional success rate, demonstrating the platform’s high usability in the school environment. Respondents provided overwhelmingly positive feedback, affirming the successful implementation and functionality of the website.
COMPARATIVE OF LSTM AND GRU FOR TRAFFIC PREDICTION AT ADIPURA INTERSECTION, BANDAR LAMPUNG Ilham Firman Ashari; Verlina Agustine; Aidil Afriansyah; Nela Agustin Kurnianingsih; Andre Febrianto; Eko Dwi Nugroho
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6569

Abstract

The Tugu Adipura intersection in Bandar Lampung is a vital traffic hub connecting four major roads. Rapid population growth and increasing vehicle numbers challenge traffic flow and urban quality of life. Despite its importance, there is limited research using predictive models to analyze traffic patterns at complex intersections in mid-sized Indonesian cities. This study addresses that gap by examining traffic growth on four connected roads using deep learning models. Traffic data were collected hourly from June 1, 2021, to July 31, 2023. A comparative analysis of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models was conducted, with SGD and Adam as optimizers. Results show the GRU model with Adam achieved the lowest RMSE (0.23) on road section 1, indicating its superior ability to model short-term fluctuations and non-linear growth in traffic volume. The study offers practical implications for traffic management by highlighting GRU’s capacity to capture seasonal trends and rapid growth, supporting proactive infrastructure planning and congestion mitigation strategies. These findings demonstrate the value of data-driven approaches in enhancing transportation systems in growing urban areas.
Peningkatan Kualitas Hidup Masyarakat Desa Margasari LabuhanMaringgai Lampung Timur melalui Pelatihan Pengelolaan Sampahdan Teknologi Pendidikan Aidil Afriansyah; Andre Febrianto; Mugi Praseptiawan; Hafiz Budi Firmansyah; Naufal Raki
Jurnal Relawan dan Pengabdian Masyarakat REDI Vol. 2 No. 4 (2025): Mei
Publisher : Yayasan REDI Tiga Monas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69773/tk72p376

Abstract

Kegiatan Kuliah Kerja Nyata (KKN) yang dilaksanakan di Desa Margasari, Kecamatan Labuhan Maringgai,Lampung Timur bertujuan untuk mengatasi permasalahan utama di bidang lingkungan dan pendidikan.Kegiatan ini melibatkan penyuluhan pengelolaan sampah dan pemanfaatan tanaman obat keluarga(TOGA), pelatihan pembuatan kerajinan dari bahan limbah, serta penggunaan teknologi informasi untukpendidikan. Hasil kegiatan menunjukkan peningkatan pemahaman masyarakat tentang pengelolaansampah dari 40% menjadi 70%, serta peningkatan kemampuan siswa dalam penggunaan komputer dari20% menjadi 70%. Dampak positif dari kegiatan ini adalah peningkatan pendapatan keluarga melaluipenjualan kerajinan dan peningkatan motivasi belajar siswa. Evaluasi kegiatan menunjukkan antusiasmemasyarakat yang tinggi dan keinginan untuk melanjutkan program serupa di masa depan. Saran untukperbaikan mencakup peningkatan durasi pelatihan dan kerjasama dengan pihak pemerintah serta sektorswasta untuk memperluas cakupan program
Evaluasi Logistic Regression dan Neural Network pada Klasifikasi Gagal Jantung Berbasis Threshold Anggraini, Leslie; Akram Abdillah, Attar; Kartadilaga, Muhammad Qaessar; Verdiana, Miranti; Nugroho, Eko; Afriansyah, Aidil; Febrianto, Andre; Bagaskara, Radhinka
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

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

Abstract

Kardiovaskular adalah sistem jantung dan pembuluh darah dalam tubuh manusia yang bertanggung jawab atas sirkulasi darah dalam jantung, pembuluh darah, dan darah sendiri. Gangguan pada fungsi sistem ini dapat menyebabkan penyakit kardiovaskular, seperti gagal jantung, yang menjadi salah satu penyebab utama kematian di seluruh dunia. Kematian yang disebabkan oleh gagal jantung mempengaruhi 1.5 juta pasien di seluruh dunia. Dikarenakan oleh data statistik tersebut, maka ada kebutuhan untuk dapat memprediksi dampak gagal jantung untuk membantu tingkat kelangsungan hidup pasien. Sebagai bentuk kontribusi terhadap kebutuhan tersebut, penelitian ini akan menganalisis sebuah dataset pelayanan kesehatan, yaitu dataset rekam gagal jantung dari UCI. Dataset tersebut akan digunakan untuk mengklasifikasi dan memprediksi peluang kematian dari pasien gagal jantung. Kami akan membandingkan antara dua metode klasifikasi dari machine learning, yaitu Logistic Regression (LR), dan deep learning, yaitu Shallow Neural Network (SNN). Mutual Information (MI) dipilih sebagai metode pemilihan fitur. Hasil menunjukkan bahwa SNN menghasilkan akurasi lebih tinggi dengan skor 0.75, dibandingkan LR dengan akurasi sebesar 0.63.
Analisis Hubungan dan Prediksi Depresi Mahasiswa Berdasarkan Faktor Akademik dan Gender Verdiana, Miranti; Dwi Nugroho, Eko; Anggraini, Leslie; Bagaskara, Radhinka; Yulita, Winda; Afriansyah, Aidil; Habib Algifari, Muhammad
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 10 No. 1 : Tahun 2025
Publisher : LPPM UNIKA Santo Thomas

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

Abstract

This study aims to analyze the level of depression among university students by examining gender and several academic indicators. The dataset includes responses from 27,901 students across various regions, with variables covering age, gender, academic pressure, study satisfaction, work/study hours, CGPA, and depression status. The analytical methods applied in this study include the chi-square test to eval_uate the association between gender and depression status, point-biserial correlation to examine relationships between numeric variables and depression, and logistic regression to develop a prediction model. The chi-square test results revealed no significant relationship between gender and depression (p = 0.774), indicating that depression affects both genders. In contrast, academic pressure exhibited the strongest correlation with depression status (r = 0.47), followed by work/study hours (r = 0.209) and study satisfaction (r = -0.168). The Logistic Regression model constructed using the four most relevant variables demonstrated satisfactory performance, achieving 75.5% accuracy and 82.1% recall in identifying students experiencing depression. These findings highlight the critical role of academic-related factors—particularly academic pressure—in influencing students’ mental health. Therefore, targeted academic support strategies are essential to mitigate depression risks in higher education environments. Keywords— Student Depression, Academic Pressure, Gender, Logistic Regression, Mental Health Prediction
Co-Fit: Menjaga Kesehatan melalui Aplikasi Health Profiling dalam Pencegahan dan Deteksi Gejala Covid-19 Afriansyah, Aidil; Annisa, Resty
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 6 No. 2 : Tahun 2021
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/jtiust.v6i2.1500

Abstract

Menjaga kesehatan kebutuhan utama manusia khususnya sekarang pada saat pendemi COVID-19. Setiap manusia memiliki kebutuhan yang berbeda dalam menjaga kesehatan. Dimasa pandemi mengharuskan kita membatasi kegiatan diluar rumah namun tetap menerapkan pola hidup sehat diantaranya adalah berolahraga dalam upaya untuk menjaga kondisi kesehatan, penulis terinspirasi membangun sebuah sistem yang dapat mendukung hal tersebut. Sistem yang dibangun harus dapat memahami profil kesehatan (Health Profiling) dari user. Setiap hari user mendapat notifikasi untuk melakukan pengecekan kesehatan dengan cara menjawab beberapa pertanyaan tentang kesehatan khususnya gejala COVID-19, Jika user terdeteksi gejala COVID-19, akan ditampilkan notifikasi segera melakukan pengecekan ke klinik kesehatan terdekat. Setelah terdeteksi gejala dini COVID-19 oleh pihak klinik, system akan memberikan informasi rekomendasi ke rumah sakit rujukan COVID-19. System ini juga dapat memberikan informasi cara menjaga kesehatan dengan pemberitahuan untuk berolahraga beserta program latihannya (exercises) dan informasi terbaru mengenai COVID-19. Hal ini sebagai tindakan deteksi dini pencegahan COVID-19. Metode yang digunakan pada perancangan system ini yaitu User-Centered Design yang mengacu pada user experience calon pengguna. Tujuan utama dari metode tersebut adalah untuk membuat suatu sistem informasi yang user-friendly dengan tingkat usability yang tinggi
Co-Authors Agung Mahadi Putra Perdana Akram Abdillah, Attar Alfajrin, Achmad Chalid Afif Algifari, Muhammad Habib Andika Setiawan Andre Febrianto Anggraini, Leslie Annisa, Resty Apri Kurniawansyah Arham, Laode Bagaskara, Radhinka Chalid, Achmad AA Dani Hamdani Hamdani David Gunawan, David Duwi Harianto Duwi Hariyanto Dwi Nugroho, Eko Eko Dwi Nugroho Eko Nugroho Ermida Silitonga Fajar Paundra Febrianto, Andre Firmansyah, Hafiz Budi Firmansyah, Hafiz Budi Habib Algifari, Muhammad Ichwani, Arief Idris, Mohamad Ilham Firman Ashari Kartadilaga, Muhammad Qaessar Kisna Pertiwi Kisna Pertiwi, Kisna Lathifa Putri Afisna Lorini, Valerio M. Rizky Hikmatullah M. Yafi Fahmi Miranti Verdiana Mufidah, Zunanik Muhammad Nadhif Athalla Naufal Raki Nela Agustin Kurnianingsih Nur'azmi, Eka Praseptiawan, Mugi Radhinka Bagaskara Rahman Indra Kesuma Resty Annisa Resty Annisa Resty Annisa Annisa Rinaldi, Daniel Rio Ariestia Pradipta Rizkita, Alya Khairunnisa Sabar Sabar Sabar Solly Aryza Sophia Nouriska Untoro, Meida Cahyo Utoro, Meida Cahyo Vebriyanto, Mario Verdiana, Miranti Verlina Agustine Winarta, Richard Arya Winda Yulita Yoga Naufal Ray Putro Yulita, Winda