cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Irfan Mujahidin
Contact Email
irfan.mujahidin@unmer.ac.id
Phone
-
Journal Mail Official
Irfan.mujahidin@unmer.ac.id
Editorial Address
-
Location
Kota malang,
Jawa timur
INDONESIA
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer)
Published by Universitas Merdeka Malang
ISSN : 26565722     EISSN : 2685497X     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics
Sistem Tenaga Generator, Distribusi Daya, Konversi Daya Listrik, Sistem Perlindungan dan Teknologi Bahan elektrik Sinyal, Sistem, dan Elektronik Pemrosesan Sinyal Digital, Pemrosesan Gambar, Sistem Robot, Sistem Kontrol dan Sistem Embeded Sistem komunikasi Telekomunikasi, Komunikasi Nirkabel dan Jaringan Komputer Teknologi Informasi Rekayasa Perangkat Lunak, Penambangan Data Multimedia, Komputasi Bergerak, Komputasi Paralel / Terdistribusi, Inteligensi Buatan, Grafik Komputer dan AR / VR Aplikasi Sains Instrumentasi, Matematika, Fisika, Teknologi Geologi, Kimia, Pendidikan Sains dan Teknologi Kesehatan atau Biomedis.
Arjuna Subject : -
Articles 129 Documents
 9   10   11   12   13 
Penerapan Naive Bayes pada Sistem Pakar Pendeteksi Jaringan Internet di Rosi Cell.Net Sigit Nur Ervansah; Abdi Pandu Kusuma; Yusniarsi Primasari
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 6 No. 2 (2024): Desember 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v6i2.14049

Abstract

Rosi Cell.Net di Kabupaten Nganjuk menghadapi tantangan berupa kerusakan jaringan fisik dan gangguan perangkat lunak yang diperburuk oleh cuaca dan interferensi elektromagnetik. Untuk mengatasi ini, sistem pakar berbasis metode Naive Bayes dapat digunakan untuk mendiagnosis dan menyelesaikan masalah komputer dan jaringan secara efisien, sehingga meningkatkan keamanan dan efisiensi operasional. Penelitian ini bertujuan mengembangkan sistem pakar menggunakan algoritma Naive Bayes untuk mendeteksi dan mengklasifikasikan kerusakan jaringan di ROSI CELL.NET. Penelitian menggunakan metode deskriptif kualitatif. Hasil penelitian menunjukkan bahwa implementasi metode Naïve Bayes dalam sistem pakar untuk deteksi masalah jaringan di ROSI CELL.NET memberikan hasil positif, dengan akurasi 85,71%, presisi 90%, recall 72%, dan F1-Score 80%. Meskipun ada beberapa hasil yang tidak sesuai dengan data latih, metode ini tetap efektif dalam meningkatkan efisiensi troubleshooting dan kepuasan pelanggan, terutama dalam mendiagnosis masalah kabel LAN dengan probabilitas 0,8268.
Pengukuran Skala Prioritas Data Logistik Bencana dengan K-Means Cluster dan Skyline Query Vega Purwayoga; Hen Hen Lukmana; Winda Ayu Anggraini
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 6 No. 2 (2024): Desember 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.14067

Abstract

Analisis data logistik diperlukan untuk manajemen barang logistik secara efisin. Penelitian ini bertujuan untuk menerapakan algoritma clustering yaitu K-Means untuk mengelompokkan data logistik dengan memperhatikan aspek temporal. Penelitian ini tidak hanya mengelompokkan barang logistik, namun juga merekomendasikan waktu yang tempat untuk menyediakan barang. Data yang digunakan yaitu data barang logistik BPBD Kabupaten Purbalingga tahun 2020. Algoritma K-Means digunakan untuk mengelompokkan barang logistik pada setiap bulan yang berada terdapat pada tahun 2020. Rata-rata kualitas cluster yang dihasilkan K-Means setiap bulannya adalah 95.5 %. Tren setiap bulan hasil pengelompokkan K-Means dianalisis lebih lanjut untuk merekomendasikan waktu yang tepat untuk menambah stok barang logistik di BPBD.  Proses rekomendasi dibantu dengan algoritma skyline query dengan memanfaatkan suatu preferensi. Preferensi yang digunakan yaitu mencari bulan yang memiliki stok minimum, dan pengeluaran minimum. Bulan yang direkomendasikan untuk pengadaan barang yang termasuk ke dalam cluster C3 terdapat lima 5 bulan, sedangkan C2 sebanyak sepuluh bulan.
Implementasi You Only Look Once (YOLO) dalam Deteksi Telur Menetas pada Reptil Isa Mahfudi; Ahmad Rozak Setia Nugraha; Azam Muzakhim Imammuddin
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 6 No. 2 (2024): Desember 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v6i2.13525

Abstract

Saat ini reptil dijadikan hewan peliharaan karena perawatan yang mudah dan warna motif yang beragam salah satunya adalah Leopard Gecko. Perawatan Leopard Gecko yang baru menetas berbeda dari yang dewasa karena Leopard Gecko yang baru menetas harus segera ditempatkan di kandang yang diberi alas tisu dan disemprot air untuk menghindari kehilangan air. penelitian ini bertujuan untuk mengembangkan sistem deteksi telur menetas menggunakan YOLO (You Only Look Once). Hasil penelitian menunjukkan bahwa algoritma YOLO dapat digunakan untuk mendeteksi telur Leopard Gecko menetas secara real-time. persentase keberhasilan deteksi YOLO mencapai 94,73% pada jarak kamera 25 cm, pencahayaan 512-895 lux, dan 200 epoch pelatihan atau dapat mendeteksi 54 dari 57 objek. Jadi, model YOLO yang telah dilatih sudah memiliki keandalan yang baik dalam mendeteksi telur menetas pada reptil.
Optimalisasi Mesin Hand Sanitizer Otomatis berbasis Mikrokontroler dengan Sensor Inframerah Subhiyanto Subhiyanto; Ista Rahma Nissa
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.v7i1.11568

Abstract

In 2020, the world was hit by the coronavirus pandemic, including Indonesia. According to WHO, millions were infected and thousands died. The Indonesian government responded with several policies, including learning from home. As vaccination programs progressed, some regions began entering the new normal era. Schools resumed limited face-to-face learning by following protocols set by the Joint Decree of Four Ministries (SKB 4 Kementerian). This study, based on analysis and literature review, uses the System Development Life Cycle (SDLC) method to develop an innovative solution involving infrared sensors, commonly used in automation projects. These sensors are proven to be precise, effective, and efficient in implementing automatic hand sanitizer machines. The machine supports COVID-19 health protocols by reducing physical contact and helping prevent virus transmission.
Implementasi Modified K-Nearest Neighbor (MKNN) untuk Deteksi Penyakit Anemia Putra Dwi Wira Gardha Yuniahans; Anggraini Puspita Sari; Yisti Vita Via
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.v7i1.13425

Abstract

Anemia is a condition where the hemoglobin level in the human body drops below the normal threshold. It can cause several negative effects, such as delayed psychomotor development, a higher risk of infectious diseases, and in women, the possibility of premature birth. Therefore, early detection of anemia is essential to speed up treatment and recovery. One method that can support the diagnostic process is machine learning, particularly the Modified K-Nearest Neighbor (MKNN) algorithm. MKNN is an improved of standard KNN, incorporating additional steps such as validity calculation and weighted voting, which are not present in the original version. In this study, MKNN was applied to detect anemia and achieved an accuracy of 84% using a 75:25 train-test data split and k=5. The dataset was collected from Jemursari Hospital in Surabaya, consisting of 100 patient records. These records were used to evaluate the performance of the MKNN algorithm in anemia detection.
Pemanfaatan Multi-Layer Perceptron (MLP) untuk Deteksi Kanker Fahrul Firmansyah; Anggraini Puspita Sari; Sugiarto Sugiarto
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.13438

Abstract

Cancer is one of the deadliest diseases in the world. This is because patients often do not realize the presence of cancer in their bodies, leading to delayed treatment and the cancer becoming aggressive. Early diagnosis of cancer in women is necessary since the majority of cancer patients are women. One of the markers that can be used to diagnose cancer is the anti-Mullerian hormone, accompanied by other indicators such as lifestyle, BMI, and others. Early diagnosis can utilize the Multi-Layer Perceptron (MLP) algorithm, which is currently a rapidly developing technology. By using the MLP algorithm, an accuracy of 84% is achieved on the training data and test data, with a training-to-testing data ratio of 65:35.
Prediksi Gas Karbon Monoksida dengan Jaringan Syaraf Tiruan berbasis Internet of Things Alauddin Maulana Hirzan; Charis Maulana; Sri Handayani
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i2.14356

Abstract

Carbon monoxide is a dangerous gas that can cause fatal effects in humans if inhaled in large quantities. To detect it, a model has been developed. This study proposes a prediction model using an Artificial Neural Network (ANN) algorithm to predict carbon monoxide. Of the four ANN models evaluated, the ANN-5K model showed the best performance with an accuracy of 80.18%, followed by ANN-6K with an accuracy of 77.13%, ANN-4K with 66.44%, and ANN-3K with 53.14%. When compared to linear regression, which only had an accuracy of 57.50%, the ANN-5K model was still superior. Thus, the proposed ANN-5K model proved to be more accurate and had a lower error rate compared to other models. The main contribution of this research is a prototype equipped with an ANN model to predict carbon monoxide gas
Sistem Distribusi Air Bersih Berbasis IoT dengan ESP8266 dan RTC untuk Optimalisasi Waktu Pengaliran Rama Noveliandra; Rissa Nurfitriana Handayani
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.v7i1.15264

Abstract

Efficient clean water distribution is essential to meet the needs of communities in various regions. This study aims to develop an Internet of Things (IoT)-based clean water distribution system using ESP8266 as the main controller for automatic and scheduled water flow timing. This system is able to regulate the opening and closing of taps on a schedule and allows manual control via mobile phone devices. The method used includes the integration of IoT technology with the Blynk application for real-time monitoring and control. The results of the study show that this system can optimize the use of water resources, reduce waste, and ensure a more regular distribution of clean water. In addition, the flexibility of control via mobile phones makes it easy for users to manage water distribution. This study offers an innovative solution that can be applied to improve the efficiency of clean water distribution in various regions
Desain dan Pengembangan Aplikasi Mobile Daur Ulang Sampah di Kota Surabaya dengan Metode Design Thinking Maorisha Virginia; Made Kamisutara; Achmad Zakki Falani
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.v7i1.15295

Abstract

The issue of waste management in Surabaya City remains a challenge that requires innovative, technology-based solutions. The aim of this research is to design and develop a mobile application that supports waste recycling in order to enhance community engagement and improve waste management in a more effective and sustainable manner. This study employs the Design Thinking method to formulate problem-solving approaches, understand user needs, and test the effectiveness of the proposed design. The mobile application design and development process involves five stages: empathize, define, ideate, prototype, and test. The research results in a prototype application equipped with key features such as recycling education, location-based waste pick-up and drop-off services, and a loyalty points system to reward users. The presence of this application is expected to increase public awareness of sorting and recycling waste and contribute to environmental sustainability programs in Surabaya City.
Prediksi Keterlambatan Pembayaran Mahasiswa untuk Mitigasi Risiko Cuti Menggunakan SVM Optimasi PSO Hafis Nurdin; Imam Nawawi; Anus Wuryanto; Dewi Yuliandari; Hari Sugiarto
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.v7i1.15483

Abstract

Delayed tuition payments present challenges for higher education institutions, impacting both financial stability and students’ academic progress. This study proposes a predictive model using Support Vector Machine (SVM) optimized by Particle Swarm Optimization (PSO) to identify students at risk of payment delays. The dataset includes academic and social attributes. A dot kernel SVM was evaluated using 10-fold cross-validation. Results show that PSO optimization significantly improved model performance, particularly in recall, which increased from 36.10% to 65.51%, indicating better identification of delayed payment cases. The analysis also reveals that social factors, such as employment and academic status, strongly influence prediction outcomes. These findings highlight the potential of the SVM-PSO model as a decision-support tool for early intervention, enabling institutions to mitigate dropout risks and enhance financial planning. By leveraging this approach, universities can better support students while maintaining administrative efficiency and institutional sustainability.

Page 11 of 13 | Total Record : 129

 9   10   11   12   13 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol. 8 No. 1 (2026): Juni 2026 (forthcoming Issue) Vol. 7 No. 2 (2025): Desember 2025 Vol. 7 No. 1 (2025): Juni 2025 Vol. 6 No. 2 (2024): Desember 2024 Vol. 6 No. 1 (2024): Juni 2024 Vol. 5 No. 1 (2023): Juni 2023 Vol 5, No 1 (2023): JUNI 2023 Vol. 5 No. 2 (2023): Desember Vol. 4 No. 2 (2022): Desember 2022 Vol. 4 No. 1 (2022): Juni 2022 Vol 3, No 2 (2021): DESEMBER 2021 Vol 3, No 1 (2021): JUNI 2021 Vol 2, No 2 (2020): Desember 2020 Vol 2, No 1 (2020): Juni 2020 Vol 1, No 2 (2019): Vol 1, No 2 (2019): Desember 2019 Vol 1, No 1 (2019): Vol 1, No 1 (2019): Juni 2019 More Issue