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All Journal Teknika CESS (Journal of Computer Engineering, System and Science) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) INFOKUM International Journal of Engineering, Science and Information Technology Journal of Renewable Energy, Electrical, and Computer Engineering TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Journal of Advanced Computer Knowledge and Algorithms Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering)
Safwandi Safwandi
Fakultas Teknik Universitas Malikussaleh
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 Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Comparison of the Results of the K-Nearest Neighbor (KNN) and Naïve Bayes Methods in the Classification of ISPA Diseases (Case Study: RSUD Fauziah Bireuen) Putri, Riska Yolanda; Yunizar, Zara; Safwandi, Safwandi
Journal of Advanced Computer Knowledge and Algorithms Vol 1, No 1 (2024): Journal of Advanced Computer Knowledge and Algorithms - January 2024
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v1i1.14535

Abstract

Acute Respiratory Infection or commonly called (ARI) is a disease caused by bacteria or viruses. (ARI) can attack all ages, especially children. This study aims to compare the accuracy of classification in (ARI) disease. The data used is data from patients affected by (ARI) disease at Fauziah Bireuen Hospital. K-Nearest Neighbors and Naïve Bayes can be used in the classification of (ARI) diseases. Measurement of accuracy using Confusion Matrix in the K-Nearest Neighbors method with the Eulidean Distance approach in the case of (ARI) disease classification obtained a percentage of precision of 91%, recall 84% and accuracy of 88%. While the Naïve Bayes method obtained a percentage of precision of 95%, recall 78% and accuracy of 86%. The results of the accuracy comparison of the two methods show that the K-Nearest Neighbors method has a higher accuracy rate than the Naïve Bayes method.
Sentiment Analysis of Free Online Novel Applications Using the Support Vector Machine Method Yulidayanti, Yulidayanti -; Safwandi, Safwandi; Fajriana, Fajriana
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.732

Abstract

Sentiment analysis is a study to analyze opinions and perceptions of various topics, products, or services. With the advancement of technology, people now have easier access to literary works online, including novels. The shift from offline to online reading has resulted in a large volume of review data, necessitating an automated system to classify this data. This research aims to analyze the sentiment of reviews for online novel applications using the Support Vector Machine (SVM) algorithm. The data used in this study was gathered from user reviews of the Wattpad, Noveltoon, and Joylada applications downloaded from the Google Play Store. The results show that the Wattpad application achieved 63% accuracy, 50% precision, 64% recall, and 56% F1-score, with a 41% positive and 59% negative sentiment distribution. The Noveltoon application achieved 70% accuracy, 69% precision, 73% recall, and 71% F1 score, with a 48% positive and 52% negative sentiment distribution. The Joylada application recorded 67% accuracy, 55% precision, 92% recall, and 69% F1-score, with a 57% positive and 43% negative sentiment distribution. The results of this analysis can help understand user preferences towards online novel applications and provide insights into their impact on the application's image and user interactions.
Implementation of Simple Exponential Smoothing and Weighted Moving Average in Predicting Netflix Stock Prices Sadewa, Bima; Safwandi, Safwandi; Fajriana, Fajriana
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.708

Abstract

This study aims to develop a stock price prediction system for Netflix using the Simple Exponential Smoothing and Weighted Moving Average methods and evaluate the accuracy of both methods. The system provides future stock price estimates based on historical data and includes evaluation metrics such as Mean Absolute Error and Mean Absolute Percentage Error. The implementation results show that SES achieved an MAE of 4.40 and a MAPE of 1.08%, while WMA resulted in an MAE of 8.65 and a MAPE of 2.11%. These findings indicate that SES is more effective in predicting stock prices with lower error rates, particularly for stable historical data. In contrast, WMA is more responsive to short-term trends but less accurate overall. Based on the results, SES is recommended as the developed system's primary method for stock price prediction.
Supporting Application Fast Learning of Kitab Kuning for Santri' Ula Using Natural Language Processing Methods Zaman, Qamaruz; Safwandi, Safwandi; Fajriana, Fajriana
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.713

Abstract

Education in Islamic boarding schools is one of Indonesia's traditional forms of education that teaches Islamic religious teachings, including studying the yellow classic books as the primary source of spiritual learning. However, learning the Yellow classic book is often complicated by 'ula students (early level students) because Arabic is without harakat or lines, and the material studied is very complex. To overcome these challenges, this research aims to develop a yellow Islamic classic book learning support application for 'ula students using the Natural Language Processing (NLP) method. This application has an interactive chatbot feature that helps students understand the contents of the yellow book more effectively and enjoyably. The research method includes literature study, data collection, data processing, and system development using the Sparse Categorical Cross Entropy algorithm in Natural Language Processing to improve the accuracy of chatbot responses. This application provides an innovative solution by presenting an interactive learning experience that can be accessed anytime and anywhere, thus facilitating Santri learning outside the boarding school environment. The results show that learning for 'ula students with the Natural Language Processing method is very good and easy to understand. The test shows that the accuracy of the application reaches 100% with a low error value (loss), which is 0%. It can be recognized that the effectiveness of Natural Language Processing in supporting yellow book learning, maintaining the tradition of Islamic education in the digital era, and helping teachers and parents monitor the development of students.
SISTEM PENGENALAN POLA MAD IWAD PADA CITRA AL-QUR`AN DENGAN MENGGUNAKAN METODE COSINE Munauwarah, Munauwarah; Safwandi, Safwandi; Agusniar, Cut
Jurnal Teknologi Terapan and Sains 4.0 Vol 5, No 3 (2024): Jurnal Teknologi Terapan & Sains
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tts.v5i3.19680

Abstract

Mempelajari ilmu Al-Qur'an sesuai dengan hukum dan kaidah tajwid yang benar adalah kewajiban, karena kesalahan dalam pengucapan huruf Al-Qur'an dapat mengubah maknanya. Dalam Al-Qur'an, terdapat berbagai tanda khusus seperti hukum tajwid dan hukum mad, yang umumnya dipelajari dengan bimbingan guru ahli Al-Qu`an. Namun, metode belajar langsung dari guru menjadi kurang efisien di era modern ini, yang menuntut cara lebih praktis karena keterbatasan waktu dan akses. Seiring perkembangan teknologi, dibutuhkan sistem yang mampu mengenali hukum tajwid, khususnya mad iwad, melalui citra yang diinputkan secara digital. Program ini dirancang untuk memudahkan pengenalan pola mad iwad yang terdapat di dalam Al-Qur'an. Penelitian ini menjelaskan bahwa pola dideteksi melalui beberapa tahap, yaitu resize gambar, konversi ke citra grayscale, dan dilanjutkan dengan konvolusi. Proses pendeteksian pola mad iwad, khususnya pada surah Abasa, menggunakan metode Cosine yang menghitung nilai citra berdasarkan koordinasi dan piksel citra tersebut. Hasil penelitian menunjukkan bahwa sistem pengenalan pola mad iwad pada surah Abasa ini mencapai detection rate sebesar 85%. Ini membuktikan bahwa metode Cosine efektif dan dapat diterapkan sebagai salah satu pendekatan untuk mendeteksi pola mad iwad dalam citra Al-Qur'an. Namun, efektivitas sistem ini bergantung pada kualitas citra, semakin jelas citra yang diinput, semakin mudah sistem dalam mendeteksinya.Kata kunci: Sistem pengenalan pola, Mad Iwad, Metode Cosine, Surah Abasa
Implementation of Complex Proportional Assessment Method in Determining Prioritization of Beneficiary Groups Fish Seeds in Lhokseumawe City Ramadhani, Putri Yesi; Safwandi, Safwandi; Meiyanti, Rini
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.754

Abstract

The fisheries sector in Lhokseumawe City has an essential role in the regional economy, but the limited allocation of fish seed assistance requires an efficient and objective decision-making system (SPK). This research applies the Complex Proportional Assessment (COPRAS) method to prioritize groups of fish seed aid recipients. COPRAS was chosen because it can handle quantitative and qualitative criteria and produce a clear ranking of alternatives. The system evaluates criteria such as pond area, number of members, pond condition, and group age. The results showed that the Tani Mandiri group had the highest utility value = 1, while Tani Maju Berkah obtained the lowest value = 0.655. The COPRAS method effectively provided accurate and transparent recommendations in determining beneficiaries. Implementing this system is expected to help the Lhokseumawe City Marine, Fisheries, Agriculture, and Food Service Office allocate fairer and more targeted assistance, as well as increase the fisheries sector's productivity in the area. This research also contributes to developing technology-based decision-making systems to support government policies.
Optimalisasi Lokasi Pembangunan Stasiun Pengisian Kendaraan Listrik Umum (SPKLU) Menggunakan Sistem Informasi Geografis Di Kota Medan Dengan Metode Analisis Buffer Veirrel, Dwi Harsya Ramadhan; Safwandi, Safwandi; Fajriana, Fajriana
TEKNIKA Vol. 19 No. 2 (2025): Teknika Mei 2025
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.14936546

Abstract

Perubahan iklim global dan kebutuhan yang sangat mendesak untuk mengurangi emisi karbon membuat kendaraan listrik semakin menarik untuk dijadikan sebagai solusi transportasi ramah lingkungan. Namun, salah satu tantangan dalam mendukung penggunaannya ialah kurangnya infrastruktur pengisian daya yang memadai, seperti Stasiun Pengisian Kendaraan Listrik Umum (SPKLU). Penelitian ini bertujuan untuk menentukan lokasi terbaik untuk pembangunan SPKLU di Kota Medan dengan memanfaatkan teknologi Sistem Informasi Geografis (SIG) dan metode analisis buffer. Data geografis dan deimografis Kota Meidan digunakan seibagai dasar analisis, deingan meimpeirtimbangkan faktor-faktor seipeirti keipadatan peinduduk, akseisibilitas, dan keibeiradaan fasilitas umum. Meilalui analisis buffeir, zona strateigis diteintukan beirdasarkan radius peingaruh 100 meiteir, 300 meiteir, dan 500 meiteir dari titik poteinsial. Hasil peineilitian meinunjukkan bahwa peindeikatan ini eifeiktif untuk meineintukan lokasi yang ideial, meimudahkan akseis, dan meindukung eifisieinsi opeirasional. Peineilitian ini beirhasil meingideintifikasi beibeirapa lokasi strateigis untuk peimbangunan SPKLU di Kota Medan, yang diharapkan dapat meningkatkan kenyamanan pengguna kendaraan listrik dan mempercepat pengurangan emisi karbon. Sebagai rekomendasi, pemerintah daerah dan pengembang infrastruktur disarankan untuk memprioritaskan pembangunan SPKLU di zona-zona strategis yang telah ditentukan.
Co-Authors Andik Bintoro Annisa raihan asmaul husna Asri Anda Cut Agusniar Dahlan Abdullah Diana Diana Eva Darnila Fadisar, Nanda Fadlisyah Fadlisyah Fajriana, Fajriana Hamdi Akmal Lubis, Hamdi Akmal Hendra A Maryana Maryana Mirza Farhan Misbahul Jannah Mochamad Ari Saptari Muhammad Aldonny Altharizka Muhammad Fikry Muhammad Muhammad Muhammad Sadli, Muhammad Mukti Qamal Munauwarah, Munauwarah Muthmainnah Muthmainnah Novia Hasdyna Putri, Riska Yolanda Rahmad Wahyudi Ramadhani, Putri Yesi Rini Meiyanti Rizky Putra Fhonna Rozzi Kesuma Dinata Sadewa, Bima Tri Wulandari Veirrel, Dwi Harsya Ramadhan Veri Ilhadi Yenni Maulida Yulidayanti, Yulidayanti - Zaman, Qamaruz Zara Yunizar Zuraida Zuraida