Article Per Year (5 Year)
p-Index From 2021 - 2026
4.408
P-Index
This Author published in this journals
All Journal Teknika ELEKTRO SAMUDERA ITEj (Information Technology Engineering Journals) Techno Nusa Mandiri : Journal of Computing and Information Technology METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Building of Informatics, Technology and Science Jurnal Vokasi JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Ners Jurnal Tekinkom (Teknik Informasi dan Komputer) JUKI : Jurnal Komputer dan Informatika MEANS (Media Informasi Analisa dan Sistem) International Journal of Engineering, Science and Information Technology International Journal of Research in Vocational Studies (IJRVOCAS) TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Journal of Advanced Computer Knowledge and Algorithms INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Nawadeepa: Jurnal Pengabdian Masyarakat
Eva Darnila
Unknown Affiliation
Aerospace Engineering Agriculture, Biological Sciences & Forestry 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 42 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 42 Documents
Search

 1   2   3   4   5 
Implementasi Smart Village Berbasis IoT Dalam Meningkatkan Kemandirian Desa Di Kabupaten Bireuen Nunsina, Nunsina; Nurdin, Nurdin; Darnila, Eva; Fitri, Zahratul
TEKNIKA Vol. 19 No. 1 (2025): Teknika Januari 2025
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.13777632

Abstract

Saat ini, perkembangan teknologi informasi telah menjadi indikator kemajuan suatu Negara. Dalam konteks Indonesia, perkembangan teknologi informasi terjadi hampir di seluruh aspek, mulai dari penyelenggaraan pemerintahan sampai dengan kehidupan masyarakat. Hal ini dimulai sejak diberlakukannya Instruksi Presiden Nomor 3 Tahun 2003 tentang kebijakan pengembangan E-Government, penerapan ini menjadi manivestasi komitmen pemerintah dalam penyelenggaraan pemerintahan berbasis infrastuktur teknologi informasi. Kementerian Desa, Pembangunan Daerah Tertinggal, dan Transmigrasi memiliki konsep untuk mewujudkan desa cerdas. Berdasarkan pernyataan kemdes PDTT menjadi masalah utama dalam penelitian ini. Hal ini bertujuan untuk mengembangkan potensi desa dan mewujudkan kesejahteraan masyarakat melalui Pembangunan Desa. Konsep smart village di Indonesia tidak terlepas dari tiga elemen yaitu smart goverment, smart community, dan smart environment. Dengan demikian ketiga unsur tersebut harus memiliki sinergisitas yang berbasis teknologi (IoT). Diperlukan kajian lebih lanjut untuk mengetahui penerapan smart village berbasis IoT pada ketiga indikator tersebut baik ekonomi, sosial maupun lingkungan di Kabupaten Bireuen. Penelitian ini bertujuan untuk melihat bagaimana implementasi smart village berbasis IoT di Kabupaten Bireuen. Dari 20 desa percontohan yang ada di Kabupaten Bireuen, maka hasil penelitian ini merekomendasikan 3 desa di Kabupaten Bireuen yang bisa menuju smart village sesuai dengan target pemerintah yaitu desa Blang Kubu di Kec. Peudada, desa kec.Ganda pura dan desa Cot Mesjid Kec. Juli.
Independent Campus Student Exchange Sentiment Analysis Using SVM Irhami, Putri; Darnila, Eva; Fadlisyah, Fadlisyah
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 2 (2024): Journal of Advanced Computer Knowledge and Algorithms - April 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i2.14902

Abstract

Support Vector Machine (SVM) is a machine learning method that is widely used for regression and classification problems, especially application review classification. Student exchange is one of the programs that universities must prepare. The student exchange program is intended to reduce the problem of disparities in educational facilities and infrastructure in Indonesia. The advantage of student exchange is that they can manage their time, have high awareness in communicating, are able to admit when they experience problems and need help, independent student exchange offers study options of up to 20 credits, both covering Higher Education Recipients courses and activities in the form of the Nusantara Module. Additionally, students are offered the option to register for a maximum of 6 credits of higher education online. The method used in this research is the SVM algorithm, the dataset used consists of 1000 comment reviews with a ratio of 70;30. This research was implemented in a web system using the Python programming language. Of the 300 test data implemented with 700 training data. The Support Vector Machine (SVM) algorithm in classifying review data obtained the highest accuracy in dividing training data & test data 70:30 at 85.00% then precision 28.33%, recall 33.33%
KLASIFIKASI WILAYAH RAWAN PANGAN DI KABUPATEN ACEH UTARA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR Eva Darnila; Maryana Maryana; Khairunnisa Khairunnisa
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.1075

Abstract

Food insecurity is a condition where food security is not achieved, so food insecurity can be interpreted as a condition of not providing enough food for individuals or individuals to be able to live healthy, active, and productive lives sustainably The Department of Agriculture and Food of North Aceh Regency has compiled a map of the FSVA of North Aceh Regency but it is known that the process of collecting and summarizing FSVA data takes a long time thus resulting in slow handling. This study classifies regional data to classify food insecurity priorities quickly and efficiently using the K-Nearest Neighbor (KNN) algorithm. The data used in this study were 852 regional data. Then it is grouped into 6 priorities, namely Priority 1, Priority 2, Priority 3, Priority 4, Priority 5, Priority 6. The data is divided into 2, namely 70% is used for training data and 30% is used as test data. The results of the classification of food insecure areas using the K-Nearest Neighbor algorithm are Priority 1 consisting of 7 villages (2.73%), Priority 2 consisting of 24 villages (9.37%), Priority 3 consisting of 4 villages (1.56%) with the Euclidean Distance approach, this study achieved an accuracy rate of 86%.
Implementation of Organic and Inorganic Waste Selection System Based on Internet of Things Using MQTT Protocol at Abby Lhokseumawe Hospital Julita, Rina; Darnila, Eva; Risawandi, Risawandi
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.826

Abstract

The waste sorting system designed for Abby Hospital in Lhokseumawe aims to improve the efficiency and effectiveness of waste management by automatically separating organic and inorganic materials. This system integrates Proximity sensors as the primary detectors, capable of detecting organic objects within a spatial range of 4 cm and inorganic objects within a range of 5 cm. The main feature of this system is its ability to automatically sort waste, which helps reduce the potential for human error in waste categorization and improve operational efficiency in the waste disposal process. During the testing phase, which focuses on assessing the trash bin's capacity when complete, the system uses ultrasonic sensors to measure and monitor the waste filling levels. The test results show an average data transmission delay of 445.33 ms, which is within the acceptable tolerance for this system. Additionally, the prototype is equipped with an operational status notification feature for users. This notification is delivered with an average delay of just 402.5 ms, ensuring that system status information is provided to users in real time. The combination of sensor detection precision and response speed in the waste sorting process highlights the system's effectiveness in improving waste management at the hospital. This system is expected to support the hospital's efforts in maintaining a clean environment and contribute to a more environmentally friendly and organized waste management program.
Data Mining Analysis for Clustering the Number of Tb Patients in North Aceh Health Centers Using the Spectral Method Clustering Khainesya, Khainesya; Darnila, Eva; Risawandi, Risawandi
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.847

Abstract

Tuberculosis (TB) is one of the infectious diseases that is a significant concern in the world of health, especially in the North Aceh region. Grouping the number of TB patients based on severity and region is very important to support decision-making in further prevention and treatment efforts. This study applies the Spectral Clustering method to cluster the number of TB patients at Baktiya Health Center, Bayu Health Center, and Lhoksukon Health Center to identify patient distribution patterns based on severity categories. The system built is a web-based data mining analysis system using PHP and MySQL as a database. Clustering is done by dividing patients into three categories, low, medium, and high, based on five main criteria, namely age, gender, month of treatment, diagnosis results, and patient address. The results showed that Lhoksukon Health Center had the highest number of TB patients, with 136 patients (37.06%), an average age of 48.6 years, and the most cases occurred in December 2022. Bayu Health Center was at a moderate level with 130 patients (35.42%), most of whom were 45.5 years old, and most cases occurred in November 2023. Meanwhile, Baktiya Health Center had the lowest number of patients, 101 (27.52%), with the most cases occurring in November. From the clustering results, it can be concluded that the Spectral Clustering method can group TB patients well to help medical personnel and related parties develop more effective intervention strategies based on the region and severity of the patient.
Machine Learning Klasifikasi Penyakit Jiwa Menggunakan Metode K-Nearest Neighbor Berbasis Web Kiram, M. Althaf; Darnila, Eva; Sahputra, Ilham
Jurnal Ners Vol. 9 No. 2 (2025): APRIL 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jn.v9i2.43319

Abstract

Gangguan jiwa merupakan masalah kesehatan yang dapat berdampak signifikan terhadap kehidupan individu jika tidak terdiagnosis dan ditangani dengan baik. Untuk mendukung deteksi dini dan mempermudah proses klasifikasi penyakit jiwa, penelitian ini mengembangkan sistem berbasis K-Nearest Neighbor (KNN) yang diimplementasikan dalam aplikasi berbasis web. Dataset yang digunakan diperoleh dari Rumah Sakit Jiwa Aceh dengan total 564 data pasien, yang mencakup gejala seperti kecemasan, penyakit persepsi, serta tingkat keparahan dalam kehidupan sehari-hari. Proses klasifikasi dilakukan melalui serangkaian tahapan, termasuk pembersihan data, normalisasi, pemilihan parameter optimal, dan evaluasi model. Dengan K=10 model diuji menggunakan confusion matrix untuk mengukur performa dengan metrik akurasi, presisi, recall, dan F1-score, yang menghasilkan nilai 100% untuk semua kategori penyakit jiwa yang diklasifikasikan, yaitu Depresi Berat, Depresi Ringan, Skizofrenia Paranoid, dan Skizofrenia Hebefrenik. Hasil ini menunjukkan bahwa metode KNN dapat digunakan secara efektif dalam mendiagnosis penyakit jiwa berdasarkan gejala yang diberikan. Selain itu, implementasi berbasis web memungkinkan akses lebih luas bagi tenaga medis dan masyarakat dalam melakukan klasifikasi awal tanpa harus bergantung sepenuhnya pada diagnosis manual. Dengan hasil yang akurat dan sistem yang responsif, penelitian ini diharapkan dapat berkontribusi dalam meningkatkan pelayanan kesehatan mental serta memberikan solusi berbasis teknologi untuk mendukung upaya deteksi dini penyakit jiwa.
Klastering Sayuran Unggulan Menggunakan Algoritma K-Means Lina Mardiana Harahap; Wahyu Fuadi; Lidya Rosnita; Eva Darnila; Rini Meiyanti
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5277

Abstract

Horticulture, especially vegetables, has great potential to be developed because it becomes a source of income for the community and small farmers in each region because Indonesia is called an agrarian country with most of them working in agriculture. Mandailing Natal Regency is the district with the largest area in North Sumatra province, but Mandailing Natal has not been able to outperform vegetable crop production in North Sumatra. Data mining methods can find interesting and invisible patterns in data sets. One of the methods is the K-Means clustering algorithm which groups data into clusters based on the similarity of data characteristics. In this study, vegetable data was clustered which aims to determine the potential commodities in each area in Mandailing Natal Regency, plants that have potential in the area will be maintained and their production increased, while vegetable crops whose production is still low will be a priority to increase their production. The research method used in this study was to collect vegetable data from the Badan Pusat Statistik in the form of data on harvested area, production, plant area, and new planting area. In addition, data collection was carried out by conducting theoretical studies in journals. The results of clustering superior vegetables using the K-Means Algorithm are in the form of potential grouping into 3 clusters, namely low, medium, and high clusters and the output is a web-based system in its application. The results of the clustering analysis were obtained with each total data of 69 data, namely big chili with C1 81%, C2 16% and C3 3%. Cayenne C1 29%, C2 48% and C3 23%. Long Beans C1 26%, C2 38% and C3 36%. Kale C1 39%, C2 36% and C3 25%. Eggplant C1 43%, C2 29% and C3 28%. Tomato C1 41%, C2 58% and C3 1%.  
Pemanfaatan Ampas Tebu Menggunakan Bio Komposer EM4 Sebagai Pupuk Kompos Di Desa Baloy Kecamatan Blang Mangat Kota Lhokseumawe Nazimah, Nazimah; Safrizal, Safrizal; Nilahayati, Nilahayati; Darnila, Eva; Nunsina, Nunsina; Ichsan, Ichsan
Jurnal Vokasi Vol 8, No 3 (2024): November
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/vokasi.v8i3.5729

Abstract

Pemupukan merupakan salah satu teknologi yang perlu mendapatkan perhatian khusus untuk meningkatkan kualitas dan kuantitas hasil tanaman. Bahan organik merupakan salah satu unsur yang dapat menyuburkan tanah agar menghasilkan tanah yang subur, maka diperlukan bahan organik.. Alternatif yang dapat mencegah  menurunnya  kualitas tanah, adalah dengan pemberian kompos yang diharapkan dapat   meningkatkan ketersedian unsur hara sebagai media tanam. Ampas tebu merupakan bahan buangan yang biasanya dibuang secara open dumping tanpa pengolahan lebih lanjut, sehingga akan menimbulkan gangguan lingkungan    dan    bau    yang    tidak sedap. Berdasarkan hal tersebut perlu diterapkan suatu teknologi untuk mengatasi   limbah ampas tebu yaitu   dengan menggunakan teknologi daur ulang limbah  padat  menjadi  produk kompos yang bernilai ekonomis. Kegiatan  ini  dilakukan  di  Desa  Baloy Kecamatan Blang Mangat Kota Lhokseumawe dengan metode observasi, penyuluhan, pendampingan dan demonstrasi pembuatan pupuk Kompos Ampas tebu. Upaya pemberdayaan masyarakat yang berbasis desa binaan dalam pemanfaatan ampas tebu sebagai pupuk kompos sangat efektif dilakukan  karena dapat meningkatkan  pengetahuan  dan  pemahaman masyarakat  dalam  pengelahan  ampas tebu menjadi pupuk kompos yang bisa digunakan untuk keperluan sendiri maupun diperdagangkan, sehingga manpu meningkatkan kesejahteraan masyarakat baik  dari  segi kesehatan,  maupun  dari  sudut  ekonomi.  Kegiatan ini pada akhirnya akan sangat menguntungkan  bagi masyarakat Desa Baloy , karena dapat mendapatkan meningktkan pengetahuan dan ketrampilan tentang pengolahan  ampas tebu menjadi pupuk kompos
Virtual Tour Application for Cultural Heritage in North Aceh Regency using Augmented Reality Technology Melly, Melly Yani; Darnila, Eva; Maryana
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/3s7wyh46

Abstract

Cultural heritage refers to historical objects that must be preserved through protection, development, and utilisation. In North Aceh Regency, cultural heritage preservation faces challenges such as low interest among younger generations and the lack of interactive learning media. This study aims to design a virtual tour application using Augmented Reality (AR) and Geographic Information System (GIS) technologies as an interactive medium to digitally introduce cultural heritage sites. Data were collected from the Department of Education and Culture of North Aceh and through direct observation and documentation in the field. The application integrates AR features to display 3D cultural objects and GIS to present the geographical locations accurately. The development includes user interface design, motion-based navigation, and historical information panels. Testing results show that all markers successfully displayed 3D objects with an average detection time of 3.58 seconds, a detection distance of 75.71 cm, and a rotation angle of up to 360°. The objects appeared stable, and the historical information was well presented. The main contribution of this study is the implementation of AR technology in the local context of North Aceh, which has rarely been applied. Limitations include the small number of heritage sites and testing limited to a few AR devices. Future research is recommended to expand site coverage, improve device compatibility, and add gamification features to enhance user engagement.
Implementation of an Artificial Neural Network Algorithm for Mental Illness Virtual Assistant Chatbot Development iqbal, Muhammad; darnila, eva; risawandi
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/wkj2ks31

Abstract

Mental health is a critical issue in modern society, yet access to psychological support remains limited. This study presents the development of a chatbot as a virtual assistant for individuals experiencing mental illness using the Artificial Neural Network (ANN) algorithm. The dataset was manually constructed and divided using an 80:20 ratio for training and testing. The ANN model employs one hidden layer with ReLU and softmax activation functions to classify user input into relevant mental health categories. The model achieved a training accuracy of 83.2% with a loss of 0.655, and a testing accuracy of 81.5%, indicating solid performance. Compared to rule-based methods, ANN provides better adaptability in recognizing diverse expressions and delivering context-aware, empathetic responses. This study also introduces a custom-built mental health dataset and integrates a crisis response module that is underexplored in previous research. The chatbot targets five categories of mental disorders: Schizophrenia, Bipolar Disorder, Depression, Anxiety, and Personality Disorders. Findings suggest that ANN-based chatbots can serve as reliable, accessible, and scalable early-stage mental health support tools.
Co-Authors Angaw, Mariance Wulan Asrianda Asrianda Bakti Viyata Sundawa Cut Agusniar Cut Dewi Aida Soraya Dela, Monisa Dhyra Gibran Alinda Dinata, Rozzy Kesuma Ermatita - Fadhilah, Cut Fadisar, Nanda Fadlisyah Fadlisyah Fajriana , Fajriana Fajriana, Fajriana Febrin Aulia Batubara Fuadi, Wahyu Hafiz Al-Kautsar Hafni Hafni Hafni Hafni Harahap, Lina Mardiana Hazmanan Khair Hizamrul jaen Ichsan Irhami, Putri Iwan Pahendra jaen, Hizamrul Jannah, Rauzatul Japirman Purba Julita, Rina Khainesya, Khainesya Khairunnisa Khairunnisa Khalid Mawardi Kiram, M. Althaf Lidya Rosnita Lina Mardiana Harahap Mandala, Rizki Maryana Maryana Maryana Marzuki, Sinambela Mauliza, Mauliza Melly, Melly Yani Melodi, Balqis Muhammad Azmi, Muhammad Muhammad Fikry Muhammad Iqbal Muhammad Rivai Muhammad Rizki Mukti Qamal Mutammimul Ula Nasution, Tuti Adi Tama Nazimah Nazimah Nazira Nazira Nilahayati Nunsina, Nunsina Nurahma, Syahfitri Nurdin Nurdin Nurfaiza Nursanti Nursanti Nursanti Nursanti Pahendra, Iwan Pane, Syamsul Buchori Ramadani, Suci Rauzatul Jannah Razi, Ar Rini Meiyanti Risawandi, Risawandi Ritonga, Maya Syarah Rozzi Kesuma Dinata Safrizal Safrizal Safwandi Safwandi Sahputra, Ilham Salsabila, Cut Syahira Sinambela Marzuki Suci Ramadani Syaiful Bahri Teguh Rahayu TONNI LIMBONG Ulvityatni Wahyu Fuadi Yaumil Iksan Zahratul Fitri, Zahratul Zara Yunizar