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All Journal Jurnal Ners International Journal of Engineering, Science and Information Technology JUSTIN (Jurnal Sistem dan Teknologi Informasi) Brilliance: Research of Artificial Intelligence AJAD : Jurnal Pengabdian kepada Masyarakat TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Arcitech: Journal of Computer Science and Artificial Intelligence Journal Geuthee of Engineering and Energy Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Jurnal Pengabdian Nasional (JPN) Indonesia Journal of Advanced Computer Knowledge and Algorithms
Sahputra, Ilham
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Pelatihan dan Pendampingan Teknologi Informasi Pengembangan Gampong Digital Gampong Uteunkot Berbasis Web di Kota Lhokseumawe Ilhadi, Veri; Aidilof, Hafizh Al Kautsar; Fakhrurrazi; Sahputra, Ilham; Zohra, Siti Fatimah A; Angelina, Difa
Jurnal Pengabdian Nasional (JPN) Indonesia Vol. 5 No. 3 (2024): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jpni.v5i3.1064

Abstract

This program aims to enhance information technology capabilities in Uteunkot Village, Lhokseumawe City, focusing on developing web-based digital villages. The initiative includes training and assistance for village residents to support the village apparatus in public services, archiving, and marketing for MSMEs. The training aims to facilitate archiving at the Geuchik office through digital public service and archiving socialization, accompanied by website development training for the village. The web application is designed to present relevant and beneficial information for village residents with an efficient interface. The results of the digital web training and assistance indicate that villages in Indonesia are now more connected and can access broader information, contributing to increased community knowledge. The digitalization of public services has accelerated administrative processes, enhanced transparency, and facilitated interactions between village governments and their residents. Additionally, the training enhances the digital skills of village officials, increasing their capacity to utilize web-based technology. The implications of this training suggest that villages can transform to be smarter and more competitive in the digital era
Implementation of Data Mining for Vertigo Disease Classification Using the Support Vector Machine (SVM) Method Jasmin, Nadya; Dinata, Rozzi Kesuma; Sahputra, Ilham
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 4 (2024): Journal of Advanced Computer Knowledge and Algorithms - October 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i4.17807

Abstract

This research aims to implement advanced data mining techniques for the classification of vertigo disorders using the Support Vector Machine (SVM) method. Vertigo, characterized by a spinning sensation, can be triggered by various factors such as nervous system disorders and inner ear infections. With the rising prevalence of vertigo patients, there is a pressing need for more effective and efficient diagnostic tools. This study was conducted at Puskesmas Jangka in Bireuen Regency, involving the collection of vertigo patient data from the years 2023-2024. The collected data underwent a comprehensive preprocessing pipeline, including data cleaning, partitioning into training and testing datasets, and subsequent implementation of the SVM algorithm. The performance of the model was evaluated using the Mean Absolute Percentage Error (MAPE), resulting in a MAPE value of 28.47%.
Pemanfaatan Teknologi Informasi Digital Untuk Meningkatkan Produktivitas Petani sahputra, ilham; Yurni, Irma; Syukriah, Syukriah; Agusniar, Cut; Nisa, Fidyatun; Achriadi Sukiman, T Sukma
Jurnal Malikussaleh Mengabdi Vol. 3 No. 2 (2024): Jurnal Malikussaleh Mengabdi, Oktober 2024
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v3i2.20424

Abstract

Pemanfaatan teknologi informasi digital menjadi solusi strategis dalam mendukung pengelolaan pertanian dan pemasaran hasil tani. Program ini bertujuan untuk meningkatkan produktivitas petani melalui pengenalan dan implementasi aplikasi berbantuan teknologi informasi. Pelatihan ini dilakukan secara bertahap, dimulai dari seminar dan lokakarya, demonstrasi langsung, hingga pendampingan praktik lapangan. Peserta pengabdian dibekali kemampuan dalam menggunakan aplikasi untuk melihat produktivitas petani waktu tanam dan jadwal tanam. Selain itu, aplikasi web yang dijelaskan memungkinkan petani memasarkan produk secara langsung kepada konsumen tanpa perantara, sehingga meningkatkan pendapatan. Kegiatan ini juga mendukung adopsi metode pertanian yang lebih ramah lingkungan. Hasil pengabdian ini menunjukkan kenaikan peserta lebih mengetahui dan lebih percaya diri dalam menggunakan teknologi, meskipun terdapat tantangan seperti literasi digital yang rendah dan akses internet yang terbatas. Solusi berupa pelatihan intensif, penyediaan hotspot internet sementara, dan panduan digital mampu mengatasi kendala tersebut. Dengan pendekatan menyeluruh, kegiatan ini berhasil mentransformasi sektor pertanian menjadi lebih modern, kompetitif, dan berkelanjutan, memberikan dampak positif signifikan terhadap efisiensi operasional dan kesejahteraan petani petani garam.
Decision Support System for Plantation Land Suitability Assessment Using A Combination of AHP (Analytical Hierarchy Process) and Profile Matching Method Sahputra, Ilham; Fitria, Rahma; Sukia, Sukia
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 4 No. 2 (2024): December 2024
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v4i2.11957

Abstract

Determining the suitability of plantation land is a crucial factor in enhancing productivity and sustainability in the agricultural sector. However, existing studies often lack comprehensive approaches that integrate both the prioritization of criteria and precise evaluation of land suitability. This study addresses this gap by developing a decision support system (DSS) for plantation land suitability using a combination of the Profile Matching and Analytic Hierarchy Process (AHP) methods. The AHP method is employed to assign weights to various criteria based on their relative importance, while the Profile Matching method evaluates land suitability based on the generated profiles.  The results indicate that this integrated approach provides accurate and detailed land suitability recommendations. Specifically, Buket Rata land is suitable for Clove (preference score: 3.821), Oil Palm, and Tea (3.596); Reulet land is suitable for Cocoa (3.22) and Coconut (3.16); Geulanggang Kulam land is suitable for Clove (3.41), Cocoa (3.35), and Oil Palm (3.29); Sawang land is suitable for Clove (3.41), Oil Palm (3.17), and Cocoa (2.99); and Pesisir Laut land is suitable for Sugarcane (3.353) and Clove (3.173). This DSS not only aids decision-makers in optimizing land use and managing sustainable plantations but also contributes to the broader field of agricultural decision-making by demonstrating the effectiveness of combining AHP and Profile Matching methods.
Automated Recognition of Batik Aceh Patterns Using Machine Learning Techniques Utaminingsih, Eka; Sahputra, Ilham
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4831

Abstract

This research focuses on the automatic recognition of Aceh batik patterns using machine learning techniques. Utilizing a Convolutional Neural Network (CNN) model based on EfficientNet, a dataset consisting of 1,200 Aceh batik images was processed through various stages, from data collection to model training and evaluation. The images are divided into three main classes: Bungong Jeumpa, Ceplok, and Kerawang. The data processing steps include normalization, resizing, and data augmentation to ensure better variation. The model was trained using 75% of the data as a training set and 25% as a testing set. The results indicate that the model performed excellently, achieving an accuracy rate of 98%. According to the classification report, the model achieved an average precision, recall, and F1-score of 0.98. The Kerawang category achieved the highest precision at 100%, while the Bungong Jeumpa and Ceplok categories had F1-scores of 0.98 and 0.97, respectively. These findings demonstrate the potential of machine learning methods in recognizing Aceh batik patterns with high accuracy, supporting the preservation of local culture through technology.
Sistem Pakar Diagnosa Penyakit Tanaman Kelapa Sawit Menggunakan Metode Certainty Factor (Studi Kasus: Pt Evans Simpang Kiri Plantation Aceh Tamiang) Elvanni, Imelda Elvanni; Pratama, Angga; Sahputra, Ilham
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 1 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i1.83511

Abstract

Kelapa sawit merupakan tanaman penting yang berperan besar dalam perekonomian dan industri banyak negara, termasuk Indonesia. PT Evans Simpang Kiri Plantation di Aceh Tamiang mengalami fluktuasi produksi kelapa sawit selama empat tahun terakhir, dengan puncaknya pada tahun 2022 sebesar 52.878 ton dan penurunan pada tahun 2023 menjadi 51.131 ton akibat serangan penyakit. Penurunan produksi ini menekankan pentingnya sistem diagnosa penyakit untuk menjaga produktivitas kelapa sawit. Dalam penelitian ini, Metode Certainty Factor  (faktor keyakinan) digunakan untuk mendiagnosis penyakit kelapa sawit oleh sistem pakar berbasis web. Sistem ini diharapkan dapat memberikan diagnosis cepat dan akurat, membantu pengendalian penyakit, serta meningkatkan efisiensi dan produktivitas perkebunan kelapa sawit. Penelitian ini mengambil studi kasus di PT Evans Simpang Kiri Plantation Aceh Tamiang, dengan tujuan utama merancang sistem profesional yang dapat menemukan dan mengobati penyakit kelapa sawit. Hasil penelitian diharapkan dapat mendukung PT Evans Simpang Kiri Plantation dalam meningkatkan produksi kelapa sawit dengan mengatasi masalah penyakit secara efektif dan efisien. Sistem ini mencakup 31 gejala dan 6 aturan, dengan hasil salah satu petani yang tanaman kelapa sawitnya didiagnosis memiliki penyakit Busuk pangkal batang (basal stem rot / ganoderma) dengan nilai kepastian 100%.    Kata kunci: Kelapa sawit, PT Evans Simpang Kiri Plantation, diagnosis penyakit, sistem pakar, Certainty Factor, gejala penyakit, pengendalian penyakit.
Analysis Of The Performance Of Junior High Schools In The Nisam Sub-District Using The Data Envelopment Analysis Method Mudawali, Mudawali; Abdulllah, Dahlan; Sahputra, Ilham
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.v5i2.823

Abstract

Education plays a major role in improving the quality of human resources. To support this effort, an application is needed so that it can measure the efficiency of education performance quickly and automatically. This research uses the Data Envelopment Analysis (DEA) method. The Data Envelopment Analysis method is a method that utilizes linear programming to compare decision-making units, by comparing one Decision Making Unit (DMU) with other DMUs that use similar resources to produce similar outputs. This research successfully developed an application to measure the performance efficiency of junior high schools in Nisam District. The application was designed using UML, PHP, and MySQL, and data was collected through interviews with school officials. The application provides an attractive user interface and can calculate linear programming with the DEA method, in accordance with calculations using Lindo 6.1 software. The data used for the efficiency analysis includes input data, such as the number of teachers, other students, facilities and infrastructure, the number of students admitted in 2023, certified and uncertified teachers, teachers with master's degrees, PPPK teachers, honorarium teachers, as well as the average student pass rate and the number of students graduating in 2023 as output. Of the 6 sample schools, 5 schools (83.33%) achieved efficiency, while 1 school (16.66%) was not efficient.
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.
Optimisation of Employee Attendance System Using Face Recognition and Geotagging Based on Mobile Android Fitria, Rahma; Sahputra, Ilham; Maulana, Riki
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6892

Abstract

The growth of technology is developing very rapidly in various fields, ranging from industry, offices, government, to education. One interesting innovation is the application of facial recognition to an Android-based attendance system. This system allows attendance to be carried out by scanning employee faces using Android devices in certain areas such as offices or companies. By using this technology, the attendance process which is usually done manually or using fingerprints can be optimized, thereby reducing the risk of long queues when employees are present together. In some offices, attendance is still done manually by filling in attendance books or using fingerprints. This method often causes problems, especially when many employees come at the same time. The queues that form will of course interfere with their productive time. Therefore, to overcome this problem, an Android-based attendance application is needed that integrates facial recognition technology. This application is designed so that it can only be accessed in an office environment, with certain area coverage settings. This study uses the Convolutional Neural Network (CNN) algorithm which is effective for image processing in facial recognition. In addition, researchers also apply the GPS Locking or Geotagging method to ensure that attendance can only be carried out in predetermined areas, thereby increasing the security and accuracy of attendance data. The dataset used in this study consists of facial images, where each individual is photographed in five different angles to improve the accuracy of the system. The results of this study are expected to create a more efficient and effective attendance system. By simplifying the attendance process, this technology not only saves time but also increases employee satisfaction, because they no longer have to face long queues. This is a step forward in utilizing technology to improve human resource management in the digital era.
Decision Support System for Potential Stock Selection Recommendations Using AHP and Profile Matching Methods Sahputra, Ilham; Ilhadi, Veri; Pratama, Angga; Syukriah, Syukriah; Arifa, Tiara Minda
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.5981

Abstract

This study presents the design and implementation of a Decision Support System (DSS) aimed at facilitating the selection of potential banking stocks by novice investors. The system integrates two well-established decision-making methodologies: the Analytical Hierarchy Process (AHP) and Profile Matching. The objective is to provide a structured, data-driven approach that assists users in making informed and objective investment decisions based on critical financial performance indicators. These indicators include Price to Earnings Ratio (PER), Price to Book Value (PBV), Return on Assets (ROA), Return on Equity (ROE), Earnings Per Share (EPS), Book Value Per Share (BVPS), Debt Ratio (DR), and Dividend Yield (DY). In this system, AHP is employed to calculate the relative weight or importance of each financial criterion through pairwise comparisons, incorporating users judgment in the weighting process. Once the weights are determined, the Profile Matching method is used to assess and rank the alternative banking stocks based on how closely they align with the ideal profile defined by the criteria. The results of the analysis identified Bank Mandiri (BMRI) as the top-ranked stock, followed by Bank Rakyat Indonesia (BBRI) and Bank Central Asia (BBCA), indicating their strong fundamental performance according to the selected indicators. To validate the system's functionality, black-box testing was conducted on 21 different modules, all of which yielded valid outcomes. This confirms that the application operates correctly and reliably. Overall, the study concludes that the DSS is effective, user-friendly, and valuable as a decision support tool, especially for beginner investors targeting the banking sub-sector.
Co-Authors A Zohra, Siti Fatimah Abdulllah, Dahlan Aidilof, Hafizh Al Kautsar Alfarisyi, Ahmad Angelina, Difa Angga Pratama ANNISA KARIMA Arief Rahman Arifa, Tiara Minda Badriana, Badriana Bakhtiar Bakhtiar Burhanuddin Burhanuddin Cut Agusniar Devi Irwansyah Elvanni, Imelda Elvanni Eva Darnila Eviyanti, Cut Yuniza Fakhrurrazi Fauzi, Sri Wahyuni Fidyatun Nisa Fuadi, Wahyu Gunandra Siregar, Putri Herdiyanti, Alya Jasmin, Nadya Kiram, M. Althaf Maryana Maryana Maryana Maryana, Maryana Mudawali, Mudawali MUHAMMAD HABIB Muhammad Ikhwanus Mukarramah Mukarramah, Mukarramah Mukti Qamal Mutammimul Ula Muthmainnah Muthmainnah Nur Faliza Nurdin Nurdin Putri, Cindy Anindia Rahma Fitria, Rahma Razif, Razif Riki Maulana Rizky Putra Fhonna Rozzi Kesuma Dinata Safitri, Deviyan Safwandi Safwandi, Safwandi Saptari, Mochamad Ari Sari, Indah Maulida Sayed Fachrurrazi Siregar, Dinda Saima Agustina Sofyan, Diana Khairani Sujacka Retno Sukia, Sukia Sukiman, T. Sukma Achriadi Syibral Malasyi, Syibral Syukriah Syukriah, Syukriah Tarigan, Anjasmara Teuku Multazam Usnawiah Utaminingsih, Eka Veri Ilhadi Yulisda, Desvina Yurni, Irma Zalfie Ardian Zohra, Siti Fatimah A Zulfia , Anni Zuraida Zuraida