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All Journal CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Inotera Jurnal Sisfokom (Sistem Informasi dan Komputer) METIK JURNAL Jurnal Eduscience (JES) Jurnal Informasi dan Teknologi Jurnal AbdiMas Nusa Mandiri Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Minfo Polgan (JMP) Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Algoritma Jurnal Teknologi Terapan and Sains 4.0 Dikdimas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Inovasi Masyarakat Indonesia Jurnal Rekayasa Sistem Informasi dan Teknologi Journal Of Artificial Intelligence And Software Engineering Pengabdian Pendidikan Indonesia (PPI) Jurnal Ilmu Komputer dan Sistem Informasi Portal Riset dan Inovasi Sistem Perangkat Lunak Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Rizki Suwanda
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Sistem Pendukung Keputusan Penentuan Golongan Ukt Bagi Calon Mahasiswa Baru Menggunakan Algoritma K-Nearest Neighbor Said Fadlan Anshari; Syahriani Putri Ayu; Fadlisyah Fadlisyah; Rizki Suwanda; Tri Ramdhany
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 1 (2026): Januari 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i1.271

Abstract

In continuing lectures, financial readiness is needed to finance education. Single Tuition Fee (Uang Kuliah Tunggal or UKT) is a tuition fee in one semester where there is only one type of fee collection based on the economic and social conditions of the student's parents/guardians so that each student's payment is not the same. The existence of these group differences plus the increase in the UKT group can trigger demonstrations at Malikussaleh University for new students of the class of 2023. Therefore, a decision support system is needed in grouping UKT groups. This study uses the K-Nearest Neighbor  algorithm with  a dataset of 1381 UKT data for new students class of 2023. Furthermore, a split dataset was carried out  by dividing 90% of training data and 10% of testing data. Then the attributes used consist of 13 attributes including father's income, mother's income, father's education, mother's education, father's job, mother's job, home status, house area, number of cars, number of motorcycles, number of brothers, number of working brothers, and number of younger siblings. The outputs produced in this study are classified into 7 classes, namely UKT 1, 2, 3, 4, 5, 6, and 7. The accuracy results obtained at K = 15 were 70.5% with an error value of  29.5% with the results of the number of data in UKT 1 as many as 16 people, UKT 2 as many as 38 people, UKT 3 as many as 27 people, UKT 4 as many as 32 people, UKT 5 as many as 26 people, and UKT 6, and UKT 7 as many as 0 people.
Web-Based Stroke Disease Classification System Using the Modified K-Nearest Neighbors Method Suwanda, Rizki; Bustami, Bustami; Khardawi, Muhammad
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16046

Abstract

Classification is a systematic method of grouping data based on predefined analytical rules and principles. One of the classification methods employed in this study is the Modified K-Nearest Neighbors (MKNN) algorithm, which is recognized for its potential to achieve higher accuracy. MKNN is an extension of the traditional K-Nearest Neighbors (KNN) method, incorporating an additional ranking stage and a weighted voting mechanism using an alpha value of 0.5. The object of this study is stroke disease. In the medical context, stroke occurs due to a disruption of blood flow to the brain. Ischemic stroke is caused by the obstruction of blood vessels and is generally considered less severe, whereas hemorrhagic stroke results from the rupture of blood vessels and is categorized as a severe condition. Hospitals in Indonesia are required to provide prompt and accurate healthcare services, in accordance with Law Number 36 of 2009 concerning Health. Approximately 70% of stroke patients have a history of hypertension and heart disease, while around 87% experience psychological disorders such as anxiety and depression. Based on data obtained from Cut Meutia Regional General Hospital (RSUD Cut Meutia) in Lhokseumawe, the classification of stroke types is still performed manually through clinical observation. Therefore, this study proposes a stroke classification system based on the MKNN algorithm. The system utilizes 11 features and two diagnostic classes, namely ischemic stroke and hemorrhagic stroke, with a total of 100 medical record datasets divided into 80 training data and 20 testing data. Using a value of K = 5, the system achieved an average confidence accuracy of 81.19%, with a precision of 85.71%, recall of 80%, F1-score of 82.75%, and overall accuracy of 75%. The system was developed using the PHP programming language and a MySQL database.
Comparison of Single Exponential Smoothing and Double Exponential Smoothing Methods for Gold Price Prediction mardhatillah, mardhatillah; bustami, bustami; suwanda, rizki; safwandi, safwandi; qamal, mukti
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.8597

Abstract

Emas diakui secara global sebagai safe haven asset dengan nilai yang relative stabil, meskipun harganya tetap mengalami fluktasi akibat pengaruh faktor ekonomi  seperti kondisi global, inflasi, serta keseimbangan permintaan dan penawaran. Oleh karena itu, peramalan harga emas yang akurat menjadi penting dalam mendukung pengambilan keputusan investasi. Penelitian ini bertujuan untuk membandingkan kinerja metode Single Exponential Smoothing dan Double Exponential Smoothing dalam meramalkan harga emas. Data yang digunakan berupa data deret waktu bulanan harga emas periode januari 2022 – 2024 yang diperoleh dari beberapa took emas. Sistem peramalan dikembangkan berbasis web menggunakan bahasa pemograman PHP. Evaluasi akurasi dilakukan menggunakan metode Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa kedua metode mampu memberikan prediksi yang cukup baik, namun metode SES menghasilkan nilai MAPE yang lebih rendah dibandingkan DES. Penelitian ini diharapkan dapat menjadi referensi bagi pelaku usaha emas dalam menentukan strategi investasi yang tepat  
Student Academic Consultation Chatbot Using Meta AI Large Language Models and Retrieval-Augmented Generation Pangestu, Aridho; Hamdhana, Defry; Suwanda, Rizki
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.8875

Abstract

Academic consultation is an important service for students in obtaining information related to academic regulations, procedures, and requirements. However, the consultation process, which is still carried out manually, often causes delays in the delivery of information and limited access, especially when students need answers quickly. Therefore, a system is needed that is capable of providing academic consultation services automatically and based on official documents. This study aims to design and build a student academic consultation chatbot using Large Language Model (LLM) technology and Retrieval Augmented Generation (RAG) architecture. The methods used include calling up Academic Guidelines documents, splitting text into several parts (text splitting), creating embeddings using the HuggingFace all-MiniLM-L12-v2 model, and storing embeddings in a vector database. Next, the system performs a relevant document search process using a retriever and utilizes the LLaMA 3.1-8B-Instant model to generate answers based on the context found. The chatbot's performance was evaluated using ROUGE metrics, including ROUGE-1, ROUGE-2, and ROUGE-L, with measurements of precision, recall, and F1-score. The evaluation results showed that the chatbot was able to provide relevant answers in accordance with academic documents. The average evaluation scores obtained were precision of 47,57%, recall of 67,85%, and F1-score of 53,36%. The higher recall score indicates that the system is quite good at covering reference information, although the accuracy of word selection can still be improved.
Prediction Of Industrial Waste Using The Autoregressive Integrated Moving Average Method Roslaini, Roslaini; Abdullah, Dahlan; Suwanda, Rizki
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.624

Abstract

This study presents the development of a web-based industrial waste prediction system using the Autoregressive Integrated Moving Average (ARIMA) method to forecast the volume of liquid and solid waste generated by PT Pupuk Iskandar Muda (PIM). The predictive model is built upon historical waste data collected between 2020 and 2023, serving as the foundation for the statistical analysis. The system is developed using the Flask web framework, offering an interactive and user-friendly interface, while SQLite3 is employed as a lightweight local database solution for efficient data handling. The ARIMA (1,1,1) model was selected based on stationarity testing and examining ACF and PACF patterns. The results suggest that the model can moderately capture prediction trends, although limitations in accuracy are evident. For 2024, liquid waste is projected to decrease from 30,600 tons in January to 29,400 tons in December. In contrast, solid waste displays a more stable trend, with an average monthly generation of approximately 23.2 tons. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) method, yielding high error rates—166.11% for liquid waste and 100% for solid waste, highlighting the significant impact of data quality and completeness on prediction accuracy. The system generates visual outputs through interactive graphs and tables accessible via a web browser, supporting data-driven decision-making. This research is a predictive tool for PT PIM and a reference for future development of technology-driven waste management systems to promote environmental sustainability.
IMPLEMENTASI METODE WEIGHTED AGGREGATED SUM PRODUCT ASSESSMENT (WASPAS) DALAM SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN PESTISIDA TANAMAN PADI Naufal, Muhammad; Muthalib, Muchlis Abdul; Suwanda, Rizki
Jurnal Teknologi Terapan and Sains 4.0 Vol 7 No 1 (2026): Jurnal Teknologi Terapan & Sains
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tts.v7i1.26696

Abstract

Penelitian ini mengembangkan sistem pendukung keputusan berbasis web untuk mengatasi subjektivitas dalam pemilihan pestisida pada tanaman padi di Dinas Pertanian Kabupaten Bireuen. Sistem ini menerapkan metode Weighted Aggregated Sum Product Assessment (WASPAS) untuk mengevaluasi 29 alternatif pestisida berdasarkan lima kriteria, yaitu harga (30%), volume racun per hektar (25%), ukuran kemasan (10%), masa kedaluwarsa (20%), dan luas cakupan (15%). Sistem dikembangkan menggunakan bahasa pemrograman PHP dan basis data MySQL dengan model pengembangan waterfall, serta divalidasi melalui pengujian BlackBox Testing. Hasil perhitungan metode WASPAS menunjukkan bahwa Sidabas 500 EC memperoleh nilai tertinggi sebesar 0,82936 sehingga direkomendasikan sebagai pestisida terbaik untuk pencegahan hama pada tanaman padi. Sistem yang dibangun mampu melakukan proses perangkingan secara otomatis dan menyajikan hasil secara terstruktur, sehingga mendukung pengambilan keputusan yang lebih objektif dan efisien dalam upaya meningkatkan produktivitas padi secara berkelanjutan di Kabupaten Bireuen. Kata kunci: Sistem Pendukung Keputusan, Pestisida, WASPAS, Tanaman Padi
Gamifikasi Edukasi Rumah Adat Nusantara Berbasis Android Menggunakan Algoritma Fisher-Yates Shuffle Said Fadlan Anshari; Rizki Suwanda; Rizky Putra Fhonna; Muh Fahrudin Alawi
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 9 No. 2 (2025): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2025
Publisher : Universitas Malikussaleh

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

Abstract

Indonesia memiliki kekayaan budaya yang sangat beragam, salah satunya berupa rumah adat yang mencerminkan identitas dan kearifan lokal masyarakat di berbagai daerah. Namun, minat generasi muda untuk mempelajari warisan budaya tersebut cenderung menurun akibat kurangnya media pembelajaran yang menarik dan interaktif. Penelitian ini bertujuan untuk mengembangkan aplikasi gamifikasi edukasi rumah adat Nusantara berbasis Android dengan menerapkan algoritma Fisher-Yates Shuffle sebagai metode pengacakan elemen permainan. Metode penelitian yang digunakan adalah Research and Development (R&D) dengan pendekatan kualitatif dan eksperimental. Data penelitian diperoleh melalui studi literatur dari Perpustakaan Kota Lhokseumawe, Perpustakaan Program Studi Antropologi Universitas Malikussaleh, serta sumber daring yang relevan. Aplikasi yang dikembangkan memiliki tiga fitur utama, yaitu permainan susun gambar rumah adat, kuis interaktif, dan ensiklopedia rumah adat Nusantara. Algoritma Fisher-Yates Shuffle digunakan untuk mengacak susunan puzzle dan soal kuis sehingga menghasilkan variasi permainan yang lebih menarik dan tidak repetitif. Hasil penelitian menunjukkan bahwa aplikasi yang dikembangkan mampu menjadi media pembelajaran yang interaktif, edukatif, dan menyenangkan bagi anak-anak usia sekolah dasar hingga sekolah menengah pertama. Selain meningkatkan keterlibatan pengguna dalam proses belajar, aplikasi ini juga berkontribusi dalam memperkenalkan dan menumbuhkan kecintaan terhadap budaya Indonesia melalui pemanfaatan teknologi digital.
Pemanfaatan ChatGPT dalam Penulisan Karya Ilmiah dari Ide Sampai Publikasi Muhammad Khoiruddin Harahap; Rizki Suwanda
Pengabdian Pendidikan Indonesia Vol. 3 No. 01 (2025): Artikel Periode April 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ppi.v3i01.5827

Abstract

Kemampuan menulis ini menjadi hal yang harus dipahami, tetapi tidak semua mampu menulis karya ilmiah yang berkualitas mengingat masih ada yang mengalami kesulitan berkenaan dengan kendala menulis karya ilmiah. Keterbatasan tersebut seperti penguasaan tata bahasa yang baik, keterampilan berpikir kritis, dan juga penggunaan daftar pustaka yang masih belum akurat yang menjadi tantangan utama yang harus dihadapi mahasiswa dalam menulis karya ilmiah. Tujuan pengabdian masyarakat ini adalah untuk mengetahui efektifitas pemanfaatan ChatGPT dalam penulisan karya ilmiah dari ide sampai publikasi. Kegiatan pengabdian masyarakat ini dilakukan secara daring pada hari sabtu 19 April 2025, diikuti 195 peserta dari berbagai universitas. Kegiatan pelatihan mencakup pemaparan materi, panduan teknis, dan coaching clinic penggunaan ChatGPT. Sebanyak 167 atau sebesar 85,64% memberikan respon yang baik menggunakan ChatGPT dan terdapat beberapa peserta kesulitan dalam menggunakan ChatGPT. Kesimpulan di dalam kegiatan webinar ini, bahwa teknologi ChatGPT mampu meningkatkan keterampilan menulis karya ilmiah. Kegiatan ini dapat meningkatkan pengetahuan dan pemahaman tentang struktur dan format penulisan karya ilmiah dengan memanfaatkan ChatGPT serta menumbuhkan kesadaran pemanfaatan teknologi sesuai etika dan kaidah keilmuan.
Co-Authors ., Bustami Absa, Munzir Adek, Rizal Tjut Amanda, Sherly Dwi Ananda, Ria Anggara, Aji Ayu Rosmala, Ayu Rosmala Bayu Winata, Bayu Bustami Bustami Bustami Dahlan Abdullah Dendy K. Pramudito Desi Efendi, Syahril Erna Budhiarti Fadillah, Rizky Fadlisyah Fadlisyah Fanita Fanita, Fanita Fatih Dwi Laksana Haj, Shafly Ulya Halimatus Sakdiah, Halimatus Hamdhana, Defry Husna, Nur Fazri Judijanto, Loso Kamilah, Muna Khardawi, Muhammad Laksana, Fatih Dwi Mardhatillah, Mardhatillah Muchlis Abdul Muthalib Muh Fahrudin Alawi Muhammad Daud Muhammad Fikry Muhammad Furqan, Muhammad Muhammad Iqbal Muhammad Khoiruddin Harahap Muhammad Naufal, Muhammad Muhammad Zakaria Mukti Qamal Muna, Ninal Munirul Ula Munzir Nazaruddin Ahmad Nur Mauliza Nurdin Nurdin Nurul Nafisa Pangestu, Aridho Rangkuti, Irsan Efendi Raudhatul Fazira Rivalzi, Tri Ananda Rizal Rizal Rizal Rizky Putra Fhonna Roslaini, Roslaini Rozi Kesuma Dinata Rozzi Kesuma Dinata Safwandi Safwandi, Safwandi Sagala, Wulandari Shakinahwati Said Fadlan Anshari Said Fadlan Anshari Sartika, Tiara Syahriani Putri Ayu Syibral Malasyi, Syibral Taufiq Taufiq Tri Ramdhany Tulus Setiawan Wardina Ningsih Yudhi Franata Zahra, Annisa Afrilia Zahratul Fitri, Zahratul Zalfie Ardian Zara Yunizar