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All Journal BERKALA SAINSTEK INFORMAL: Informatics Journal Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal Sistem Informasi dan Bisnis Cerdas Journal of Fisheries & Marine Journal of Informatics Development Dental Agromedis
Yanuar Nurdiansyah, Yanuar
Department Of Information Technology, Faculty Of Computer Science, University Of Jember, Jember. Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : Journal of Informatics Development

 1 
Sistem Pendukung Keputusan Tempat SPBU di Kabupaten Lumajang dengan Metode Haversine Formula dan Algoritma TOPSIS Fadhel Akhmad Hizham; Yanuar Nurdiansyah
Journal of Informatics Development Vol. 1 No. 2 (2023): April 2023
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v2i2.1012

Abstract

Penentukan rekomendasi SPBU sebenarnya dapat dilakukan cukup dengan mencari jarak terdekat. Namun, jika hanya mengandalkan pemeringkatan berdasarkan jarak tempuh saja, maka tidak menutup kemungkinan adanya hal-hal yang tidak diinginkan dari SPBU yang dituju, seperti stok bahan bakar yang sedang habis, kualitas tempat yang kurang memadai, dan masih banyak lagi. Oleh karena itu, penelitian ini menggunakan beberapa kriteria dalam menentukan rekomendasi SPBU, diantaranya berdasarkan jarak, waktu tempuh, serta ulasan dari masing-masing tempat di Google Maps API, dalam hal ini digunakan Sistem Pendukung Keputusan (SPK). Penentuan urutan rekomendasi SPBU menggunakan metode TOPSIS, yang berdasarkan prioritas nilai kedekatan suatu alternatif. Dari perbandingan nilai kedekatan yang ditentukan, maka susunan urutan dari masing-masing alternatif dapat dicapai. Dalam 10 kali percobaan, dari total 700 lokasi (70 tempat SPBU dikali 10 titik awal yang berbeda), ada 76 lokasi yang memiliki pemeringkatan yang sama baik metode Haversine dengan Haversine + TOPSIS, 390 lokasi yang memiliki pemeringkatan terbaik menurut Haversine, dan 234 lokasi yang memiliki pemeringkatan terbaik menurut Haversine + TOPSIS. Untuk metode pemeringkatan Haversine + TOPSIS, ada 3 dari 10 percobaan yang memiliki tempat terbanyak dengan peringkat terbaik, yaitu percobaan ke-4 (titik awal Stasiun Klakah), percobaan ke-7 (titik awal Kebun Teh Kertowono Gucialit), dan percobaan ke-9 (titik awal Pura Mandara Giri Semeru Agung).
Decision-Making System for Selecting Alternative Product Purchase Stores in Tokopedia Using A Combination of SAW and TOPSIS Yanuar Nurdiansyah; M Zukhrofi Ardi; M Arief Hidayat
Journal of Informatics Development Vol. 2 No. 1 (2023): October 2023
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v2i1.1140

Abstract

In the era of advancing digital technology enabling online business transactions, e-commerce platforms like Tokopedia have rapidly grown in Indonesia, providing consumers with a wide array of products and services. However, this abundance of choices often leads to consumer confusion when selecting alternative stores for their purchases. Additionally, Tokopedia's search filter feature is limited, merely sorting products based on selected criteria without considering the consumer's specific needs. To address these issues, a decision-making system is essential, aiding consumers in choosing products from alternative stores that best align with their individual preferences and predetermined criteria. This study proposes a combined methodology employing the Simple Additive Weighting (SAW) method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Initially, the SAW method normalizes values (r), which are subsequently ranked using the TOPSIS method to generate recommendations for alternative stores based on input criteria and weights. The adoption of this decision-making system is poised to enhance the online shopping experience for Tokopedia users by providing well-informed recommendations, improving overall satisfaction, and streamlining the purchasing process. In conclusion, this research offers a promising approach to addressing the challenges posed by the abundance of choices in online retail, ultimately benefiting both consumers and online businesses.
Analysis of Twitter Sentiment on the Implementation of Regional Elections in Indonesia During Covid-19 Using the Support Vector Machine Method Nurdiansyah, Yanuar
Journal of Informatics Development Vol. 4 No. 1 (2025): Oktober 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v4i1.1757

Abstract

Sentiment analysis or opinion mining is a series of problem solving based on public opinion. The opinion is in the form of text or writing in the form of documents obtained from social media. Sentiment analysis serves to determine public opinion in responding to a policy, activity or issue that is happening and being discussed, one of which is on Twitter social media. Sentiment analysis in this study focuses on the activities of the 2020 regional elections during the Covid-19 pandemic which was held on 9 December 2020. Twitter social media works in real-time, so in retrieving research data using the Trending Topic feature to retrieve research datasets. The results of the dataset are then processed using text mining techniques and used as material for analysis to determine the public's response to the implementation of the elections during covid- 19 whether it tends to have a positive or negative sentiment, as well as knowing the opinion factors that often arise. The adoption of the Support Vector Machine (SVM) method for sentiment analysis was carried out by testing the composition of various datasets. From the test results using 4 scenarios of training data and test data, namely 90:10, 80:20, 70:30, 60:40, it is obtained that the SVM method can be implemented with an accuracy value of 87% in the data scenario of 80% training data and 20% test data. Variables that affect accuracy are the amount of data, the ratio of the number of training and test data and the ratio of the number of positive and negative data used.
Case Based Reasoning for Diagnosing Tuberculosis (TB) Saputri, Yunita Maulida; Nurdiansyah, Yanuar; Pandunata, Priza
Journal of Informatics Development Vol. 4 No. 1 (2025): Oktober 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v4i1.1759

Abstract

Tuberculosis, often referred to as TB, is a contagious disease caused by the bacterium Mycobacterium tuberculosis. TB primarily affects the lungs but can also affect other organs, a condition known as Extra-pulmonary TB. The disease is transmitted through the air, with the source of transmission being individuals with TB who are Acid-Fast Bacilli (AFB) positive and who sneeze or cough, releasing the bacteria into the air in the form of sputum droplets. TB can affect anyone. This research utilizes the Case- Based Reasoning (CBR) method to aid in the diagnosis of Tuberculosis. The diagnostic process involves inputting or selecting a new case that contains the symptoms to be diagnosed within the system. Then, the system calculates the similarity values between the new case and the cases stored in the case base using the Nearest Neighbor algorithm, normalized with the level of expert confidence. Testing was conducted using 50 cases from the case base and 38 new cases. The results of the system testing, using patient medical records and data obtained from literature studies, with diagnoses validated by experts, demonstrate that the system is capable of identifying 12 types of Tuberculosis with an accuracy rate of 92.3%.
Co-Authors Abd. Rasyid Syamsuri Abul Haris Suryo Negoro Ahmad Dwi Jayanto Ali, Saifur Rifqi Anang Andrianto Ari Puji Astuti Caesarina Kurnia Ananta Didin Erma Indahyani Diksy Media Firmansyah Dwiretno Istiyadi Swasono Eka Putri Agustini Wulandari Fadhel Akhmad Hizham Fatchur Rahman Firmansyah, Diksy Media Furqon, Muhammad Ariful Gama Wisnu Fajarianto Ganefo, Akhmad Hidayat, Muhamad Arief Hizham, Fadhel Akhmad Husyainus Sobri Infantono, Ardian Iron Wardana januar adi putra, januar adi Juniar Priaditama Kusnadi Kusnadi Leba, Katarina Luh Putu Ratna Sundari M Arief Hidayat M Zukhrofi Ardi Madani, Anis Muhammad Ali Sya'roni Muhammad Ali Sya’roni Muhammad Ifantara Putra Nail Pratama Negoro, Abdul Haris Suryo Nuril Ilmi Al Islami Ilmi Al Islami Prehatin Trirahayu Ningrum Priza Pandunata Puspita, Indria Dwi Rahayu, Prehatin Tri Riska Arimanudin Rizka Rohmatul Fitriyah Rizka Rohmatul Fitriyah, Rizka Rudianto S Slamin Saputri, Yunita Maulida Wardani SWW, L. Dyah Purwita Wazirotus Sakinah Yudha Alif Auliya Zarkasi, Mohammad