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All Journal Format : Jurnal Imiah Teknik Informatika Jurnal Ilmiah FIFO JURNAL TEKNOLOGI DAN OPEN SOURCE JURNAL PENDIDIKAN TAMBUSAI JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science International Journal of Engineering, Science and Information Technology Bulletin of Data Science Batara Wisnu : Indonesian Journal of Community Services Journal of Computing and Informatics Research Jurnal Ilmiah Informatika dan Ilmu Komputer Journal Of Technology and Information System (J-TIS) IPSIKOM
Sah, Andrian
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Astronomy 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 Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Pengembangan Sistem Pakar Diagnosis Jenis Stres Menggunakan Pendekatan Dempster-Shafer Theory Sah, Andrian; Heriyani, Nofitri; Jafar Rumandan, Rhaishudin; Lasiyono, M. Munawir
Journal of Computing and Informatics Research Vol 4 No 2 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/comforch.v4i2.1941

Abstract

Stress is a psychological issue commonly experienced in society and can develop into more serious disorders if not properly addressed. However, access to professional services remains limited due to constraints such as time, cost, and unequal distribution of mental health professionals. Therefore, the objective of this study is to develop an expert system using a web-based Dempster-Shafer Theory (DST) approach capable of diagnosing stress types based on user-reported symptoms. DST enables the integration of various pieces of evidence to produce conclusions with measurable confidence levels. The system is equipped with functionality for managing symptom data, stress types, and the ability to provide diagnostic results accompanied by recommended solutions. Testing results demonstrated an accuracy level of 93.33%, placing this system in the "Good" category according to standard performance evaluation classifications. The implementation of DST has proven effective in managing data uncertainty and supporting confidence-based decision-making. This research contributes to the development of DST-based diagnostic technology that can be widely accessed via a web platform, providing a reliable alternative for early detection of stress types.
Pengembangan Model Klasifikasi Citra Penyakit Daun Lada Menggunakan Jaringan Syaraf Tiruan Learning Vector Quantization (LVQ) Sah, Andrian; Mulyadi, Mulyadi; Alexander, Allan Desi; Tanniewa, Adam M
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 4 No. 1 (2025): Volume 4 Nomor 1 March 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v4i1.53

Abstract

Lada (Piper nigrum) adalah komoditas pertanian bernilai tinggi, namun rentan terhadap penyakit daun akibat infeksi jamur, bakteri, atau hama. Identifikasi dini penting untuk mencegah penurunan hasil panen, namun metode konvensional berbasis observasi visual sering subjektif dan membutuhkan keahlian khusus. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan model klasifikasi penyakit daun lada menggunakan jaringan syaraf tiruan Learning Vector Quantization (LVQ) berbasis pengolahan citra digital. Proses penelitian dimulai dengan preprocessing, yang mencakup konversi ke ruang warna CIELAB untuk meningkatkan kontras, segmentasi menggunakan Otsu Thresholding, serta ekstraksi fitur warna dengan Mean Color dan fitur tekstur menggunakan Gray Level Co-occurrence Matrix (GLCM). Hasil ekstraksi fitur ini kemudian digunakan sebagai masukan untuk algoritma LVQ, yang melakukan klasifikasi berdasarkan pembelajaran vektor prototipe. Hasil evaluasi menunjukkan bahwa model LVQ yang dikembangkan mencapai tingkat akurasi keseluruhan sebesar 90,83%. Model menunjukkan performa terbaik dalam mengenali daun sehat dengan Precision, Recall, dan F1-Score sebesar 96,67%. Sementara itu, kelas Anthracnose memiliki Precision terendah sebesar 87,01%, dan kelas Leaf Blight menunjukkan Recall terendah sebesar 86,67% serta F1-Score terendah sebesar 88,14%. Meskipun terdapat variasi kinerja antar kelas, model ini terbukti efektif dalam menangani dataset terbatas, memiliki kemampuan klasifikasi yang baik terhadap data non-linear, serta memungkinkan interpretasi keputusan klasifikasi yang lebih jelas.
PELATIHAN OPTIMALISASI PENGOLAHAN DATA KESEHATAN DI PUSKESMAS KANDA Rasna, Rasna; Nurhayati, Siti; Sah, Andrian; Tonggiroh, Mursalim; Widiyantoro, Riandi; Prasetianingrum, Septyana
Batara Wisnu : Indonesian Journal of Community Services Vol. 5 No. 2 (2025): Batara Wisnu | Mei - Agustus 2025
Publisher : Gapenas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53363/bw.v5i2.392

Abstract

Primary health care in Indonesia, especially in regions like Papua, faces major challenges in managing accurate, complete, and timely health data. At Puskesmas Kanda, data management is still done manually or semi-digitally using basic Microsoft Excel, which causes the recording and reporting process to be slow and error-prone. This condition has a negative impact on the quality of reports and data-based decision making by the management of the Puskesmas and the Health Office, contrary to the mandate of Permenkes No. 31 of 2019 concerning Health Information Systems. The purpose of this Community Service (PkM) activity is to increase the capacity of health workers at the Kanda Health Center in optimizing the use of Microsoft Excel for health data processing. The training provided included data validation techniques, the use of logical and statistical functions, the creation of visual graphs and dashboards, and the development of data recap templates that fit the real needs in the field. The results of the training showed significant improvement in participants' ability to manage and analyze health data efficiently. The evaluation showed a very high level of participant satisfaction with the benefits of the training (90%), the quality of the materials (89%), and the relevance to daily work needs (92%). With participatory learning methods and hands-on practice, the training successfully supported the digital transformation of health services.
Analisis Model Prediksi Penyakit Jantung Menggunakan Adaptive Boosting, Gradient Boosting, dan Extreme Gradient Boosting Sah, Andrian; Niesa, Chaeroen; Jafar, Rhaishudin Rumandan; Muharrom, Muhammad
Jurnal Ilmiah FIFO Vol 17, No 1 (2025)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2025.v17i1.006

Abstract

Deteksi dini penyakit jantung merupakan langkah penting untuk meningkatkan kualitas diagnosis dan perawatan pasien. Namun, metode prediksi manual yang sering digunakan tenaga medis memiliki keterbatasan dalam efisiensi waktu, akurasi, dan kemampuan menangani volume data yang besar. Dalam bidang kecerdasan buatan, algoritma machine learning seperti Adaptive Boosting (AdaBoost), Gradient Boosting, dan Extreme Gradient Boosting (XGBoost) menawarkan potensi untuk meningkatkan akurasi prediksi, terutama dalam mengatasi tantangan pada dataset kecil yang sering mengalami ketidakseimbangan kelas dan risiko overfitting. Penelitian ini bertujuan untuk menganalisis kinerja ketiga algoritma boosting tersebut dalam memprediksi penyakit jantung. Hasil penelitian menunjukkan bahwa XGBoost memberikan performa terbaik dengan akurasi sebesar 84.78% dan ROC-AUC 0.9410, menjadikannya algoritma paling efektif dalam menangani pola data yang kompleks. Gradient Boosting menjadi model paling efisien dengan waktu pelatihan tercepat, yaitu 0.3655 detik, dengan akurasi dan ROC-AUC yang kompetitif. Sementara itu, AdaBoost menunjukkan kelemahan dalam menangani ketidakseimbangan kelas tetapi tetap memberikan hasil yang baik untuk kelas mayoritas. Berdasarkan evaluasi precision, recall, dan F1-score, XGBoost direkomendasikan untuk aplikasi prediksi penyakit jantung, terutama dalam situasi yang memerlukan akurasi tinggi, sedangkan Gradient Boosting cocok untuk kebutuhan real-time.
Kombinasi Metode Rank Order Centroid dan Additive Ratio Assessment Untuk Pemilihan Aplikasi Manajemen Inventaris Tanniewa, Adam M; Sah, Andrian; Kurniawan, Robi; Prayogo, M Ari
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.6347

Abstract

Selecting an appropriate inventory management application is a challenge for business actors, especially SMEs, due to the variety of features, costs, and complexities offered. Manual selection is often carried out without a clear systematic approach and tends to be influenced by bias, resulting in suboptimal decisions. This study aims to integrate the Rank Order Centroid (ROC) and Additive Ratio Assessment (ARAS) approaches in developing a Decision Support System (DSS) to determine the best inventory management application. ROC is used to assign proportional weights to criteria based on priority ranking, while ARAS evaluates alternatives using these weights and relative utility values against the ideal solution. The developed system includes key features such as data management for criteria, alternatives, and values, as well as the ability to generate recommendations through alternative ranking. Based on a case study, the best alternative identified is Sortly: Inventory Simplified, with the highest utility score of 0.8627, followed by Housebook - Home Inventory (0.8528), inFlow Inventory (0.8336), and Inventory Stock Tracker (0.7056). Usability testing showed an average user acceptance rate of 91%, categorized as "Excellent". The main contribution of this research is the implementation of a practical and efficient combination of ROC and ARAS for selecting inventory management applications. The findings can be adopted by businesses to support more accurate and efficient decision-making.
Sistem Informasi Akademik Sekolah di SMP Advent Argapura Sah, Andrian; Jusmawati, Jusmawati
Jurnal Pendidikan Tambusai Vol. 8 No. 2 (2024)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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

Abstract

Teknologi informasi (TI) telah menjadi salah satu pengembangan penting di era globalisasi. Kebutuhan akan teknologi dan informasi menjadi hal yang sangat vital dalam segala aspek kehidupan manusia. Permintaan akan teknologi dan informasi ini muncul karena peran dan fungsinya yang membantu manusia dalam menyelesaikan pekerjaan. Organisasi dan perusahaan membutuhkan informasi yang tepat dan akurat untuk mendukung pengambilan keputusan yang berkelanjutan. Dalam sektor pendidikan, terutama di lembaga/swasta seperti SMP Advent Argapura, sistem yang dapat memproses data dan informasi dengan cepat dan mudah sangat penting. Untuk mengatasi masalah tersebut, diperlukan penggunaan sistem informasi akademik. Sistem ini akan memudahkan pengelolaan data akademik, meningkatkan efektivitas pengambilan keputusan, mengurangi kesalahan manusia, dan memungkinkan integrasi dengan sistem lainnya. Oleh karena itu, dalam penelitian ini akan dikaji implementasi "Sistem Informasi Akademik Sekolah di SMP Advent Argapura Jayapura".
SISTEM PAKAR DIAGNOSIS PENYAKIT KULIT HEWAN PELIHARAAN MENGGUNAKAN PENDEKATAN DEMPSTER-SHAFER Sah, Andrian; Septilia, Tiara Indah; Rasna, Rasna; Nurhayati, Siti; Jusmawati, Jusmawati; Tonggiroh, Mursalim; Widiyantoro, Muh. Riandi; Hakim, Jamaludin
Insan Pembangunan Sistem Informasi dan Komputer (IPSIKOM) Vol 12, No 2 (2024): DESEMBER 2024
Publisher : Universitas Insan Pembangunan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v12i2.316

Abstract

Penyakit kulit pada hewan peliharaan merupakan permasalahan yang kompleks dengan berbagai jenis dan gejala yang sering kali sulit didiagnosis secara manual. Tantangan seperti kemiripan gejala antar penyakit, ketidakpastian data, dan biaya pengobatan yang tinggi menjadi hambatan bagi pemilik hewan dalam memberikan perawatan yang tepat. Oleh karena itu, tujuannya penelitian ini dilakukan yaitu mengembangkan sistem pakar berbasis metode Dempster-Shafer yang dirancang untuk menangani ketidakpastian dalam proses diagnosis. Sistem ini memungkinkan penggabungan informasi dari berbagai gejala yang diinput oleh pengguna untuk menghasilkan tingkat keyakinan yang akurat terhadap jenis penyakit tertentu. Sistem pakar yang dikembangkan berbasis website sehingga mudah diakses kapan saja dan di mana saja oleh pengguna. Sistem ini dirancang untuk membantu pemilik hewan mengenali gejala penyakit, mempercepat proses konsultasi, serta memberikan panduan awal terkait langkah perawatan yang diperlukan. Berdasarkan hasil pengujian menggunakan metode black box testing, seluruh fitur sistem berfungsi sesuai dengan spesifikasi yang dirancang. Hasil penelitian menunjukkan bahwa sistem ini mampu memberikan hasil diagnosis, serta rekomendasi perawatan yang relevan, sehingga layak digunakan sebagai alat bantu dalam mendiagnosis penyakit kulit pada hewan peliharaan.
Geographic Information Systems of Small Industry Development During Pandemic Sah, Andrian; Prakoso, Sukma Dwi; Tonggiroh, Mursalim
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 4 No. 2 (2021): Jurnal Teknologi dan Open Source, December 2021
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v4i2.1411

Abstract

During the pandemic it caused disruption in all fields, including Small Industries. One of them is small Industries in the city of Jayapura experienced taking down of sales during the pandemi. Industry fields gave helping for small industry in Jayapura as improvement and stability to improve Small Industries during this pandemic. The Department of Industry, Trade, Cooperatives and SMEs only has information data from Small Industry Assistance and there is no development data, especially during the pandemic, development level data is needed to view and store data from observations. Therefore it is necessary to have a geographic information system to collect and provide information to small industries regarding the level of development, using the PIECES analysis method, designing using the Unified Modeling Language (UML) method, using the waterfall development method and the software used is QGIS. This research produced a Geographic Information System for the Development of Small Industries in the Pandemic as information for small industries in North Jayapura.
Geographic Information System of Patient Development in Jayapura Hospital During Pandemic Sah, Andrian; Suhardi, Suhardi; Nurhayati, Siti
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 4 No. 2 (2021): Jurnal Teknologi dan Open Source, December 2021
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v4i2.1412

Abstract

Jayapura is a city has population over 240,340 people (Source: Jayapura administration department 2021) and will be increasing in the number of patients in hospitals every year, it will have impact on all hospitals in Jayapura. With this condition, the level of patient development during pandemic and before pandemic will be known. Based on the results of data collection, it is possible to classify categories of disease and treatment in hospitals to choose the best hospital in handling disease and treating patients, especially during pandemic because health management is having difficulties because the medical team focuses more on pandemic patients. Therefore, it is necessary to have geographic information system to collect and provide information to public about the level of development of patients in hospitals, using the PIECES analysis method, designing using Unified Modeling Language (UML) method, using waterfall development method and using QGIS for software used. This research resulted the Geographic Information System for the development of patients in Jayapura City Hospital during Pandemic as means of information for patients in Jayapura.
Geographic Information System Mapping of Kerosene Businesses In Jayapura Sah, Andrian; kurniawan, Rachman; Jusmawati, Jusmawati
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 4 No. 2 (2021): Jurnal Teknologi dan Open Source, December 2021
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v4i2.1418

Abstract

The role of BBM is very important in people's lives. Fuel is a basic need for rural and urban communities, both as household needs and as business needs. The existence of kerosene in Jayapura is quite evenly distributed. From the results of survey conducted by the OFFICE OF INDUSTRY, COOPERATIVE TRADE AND SMEs that every year the use of LPG as fuel for cooking is still very low, while the use of kerosene from year to year is widely used by local community, it causes more kerosene bussiness but people have difficulty getting information about the location to get the kerosene, therefore we need a system that can help find the location, namely geographic information system that can provide information about fuel oil base points. This system is in symbols form for making the system using QGIS with the PIECES analysis method, the design using Unified Modeling Language (UML) method and the development method using waterfall method. This research produces a system that can be expected to make it easier for the public to know the distribution of kerosene in Jayapura
Co-Authors Alexander, Allan D Andika Putra Ciptaningrum, Wahyu Damuri, Amat Hakim, Jamaludin Hasma, Nur Amalia Heriyani, Nofitri Irjii Matdoan, Moh. Rahmat Jafar Rumandan, Rhaishudin Jafar, Rhaishudin Rumandan Jusmawati Jusmawati, Jusmawati Kurniawan, Rachman Lamsir, Seno Lasiyono, M. Munawir M. Ari Prayogo Muhammad Muharrom Mulyadi Mulyadi Niesa, Chaeroen Nur Hidayat, M. Ali Prakoso, Sukma Dwi Rasna, Rasna Robi Kurniawan Robo, Salahudin Sahlan M Saleh Sahrul Ponto Septilia, Tiara Indah Septyana Prasetianingrum Sidarmawan, Andri Tri Siti Nurhayati Suhardi Suhardi Suharto, Emilda Sukriyan, Mohamad Maulidin Suryo Sasono, Damar Tanniewa, Adam M Tanniewa, Adam M. Tonggiroh, Mursalim Widiyantoro, Muh. Riandi Widiyantoro, Riandi