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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JATI (Jurnal Mahasiswa Teknik Informatika) RISTEC : Research in Information Systems and Technology
Iman Hermanto, Teguh
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Computer Science & IT

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Implementation Of K-Nearest Neighbor Algorithm With SMOTE For Hotel Reviews Sentiment Analysis Gazali Mahmud, Firman; Iman Hermanto, Teguh; Maruf Nugroho, Imam
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12214

Abstract

Indonesia has considerable tourism development potential, this phenomenon is in accordance with the number of foreign tourist visits to Indonesia from January to September 2022 recorded by Badan Pusat Statistik many as 2,397,181 visitors. This research focuses on super-priority destinations in Labuan Bajo, East Nusa Tenggara, based on the government's plan that the focus of developing this destination is to increase hotel development to meet the need for an additional 2,000 hotel rooms. Thus, the available hotel rooms are still limited. Then for need to choose a hotel based on the November 2021 survey by the Populix website, 76% of 1,012 respondents chose to book hotels online with the majority using the Traveloka website. However, making decisions in choosing hotels using the reviews feature in the Traveloka website still raises various problems, such as biased information and even the rating values ​​given do not match the reviews submitted. So that users to know what becomes the perception of positive and negative ratings, it is necessary to do in-depth research on satisfaction factors to find out positive and negative sentiments of hotel visitors. This study uses the k-nearest neighbor algorithm with SMOTE on the research objects of the three most popular hotels in Labuan Bajo. Data testing uses a value of k = 3 so that it produces an accuracy value of 87.71% - 93.47% with a maximum error tolerance of 12.29%. In addition, the performance of accuracy results is validated by the appropriate AUC value, namely the good classification category.
ANALISIS SEGMENTASI PELANGGAN BERBASIS RECENCY FREQUENCY MONETARY (RFM) MENGGUNAKAN ALGORITMA K-MEANS Indra Pangestu, Panji; Iman Hermanto, Teguh; Irmayanti, Dede
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 7 No. 3 (2023): JATI Vol. 7 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v7i3.7171

Abstract

Perkembangan bisnis saat ini berkembang dengan sangat pesat, Dengan kemajuan teknologi internet yang dapat membantu segala aktivitas bisnis. Meningkatnya perkembangan bisnis berdampak menghadirkan pesaing-pesaing bisnis baru, Maka perusahaan perlu strategi yang mampu menjaga kualitas pelanggan. Penelitian ini bertujuan untuk melakukan segmentasi pelanggan dari data transaksi penjualan perusahaan, Dengan jumlah transaksi yang banyak maka diperlukan teknologi untuk mengelompokan suatu data sehingga metode yang digunakan pada penelitian ini adalah metode data mining dan menggunakan algoritma K-Means. Dengan Algoritma K-Means dapat membantu dalam pengelompokan pelanggan agar memudahkan perusahaan dalam melakukan strategi terhadap tiap-tiap kelompok pelanggan. Pengelompokan pelanggan ini menggunakan model awal Recency, Frequency dan Monetary (RFM) untuk membantu penghitungan kelompok pelanggan. Evaluasi data mining dilakukan menggunakan Silhouette Coefficient dengan hasil pengujian menggunakan software Visual Studio Code bahasa pemrogramman python, Hasil penelitian ini terpilihnya 3 cluster yang terdiri dari Low Loyalty berjumlah 137 pelanggan, Medium Loyalty berjumlah 1636 pelanggan dan Highest Loyalty berjumlah 2395 pelanggan.
Implementation of the Simple Additive Weighting (SAW) Method for Supplier Selection Muhyidin, Yusuf; Iman Hermanto, Teguh; Raymond Ramadhan, Yudhi; Irmayanti, Dede; Angga Permana, Muhammad
RISTEC : Research in Information Systems and Technology Vol. 5 No. 1 (2024): JURNAL RISTEC : Research in Information Systems and Technology
Publisher : RISTEC : Research in Information Systems and Technology

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Abstract

Supplier selection is a critical part of purchasing activities in a company because it has an impact on the quality and availability of raw materials, production cost efficiency and the smooth circulation of company finances. Determining suppliers based on certain criteria can be supported by a decision support system (DSS). The method used is the Simple Additive Weighting (SAW) method. The criteria used in selecting suppliers are speed of delivery, discount level, service, quality of goods and payment tempo. The final result obtained from the calculation process is a ranking table which shows the assessment of each supplier using the weight criteria determined by the company and recommends the supplier with the highest score as the best supplier. By implementing a decision support system using the Simple Additive Weighting (SAW) method, it can help companies determine and select suppliers who are able to provide good products. Keywords : Simple Additive Weighting Method; selection of supplier
Decision Support System for Underprivileged Scholarship Recipients Using the Simple Additive Weighting (SAW) Method in XYZ University Raymond Ramadhan, Yudhi; Alam, Syariful; Iman Hermanto, Teguh; Agus Sunandar, Muhamad; Arialdi, Miftahul
RISTEC : Research in Information Systems and Technology Vol. 4 No. 2 (2023): RISTEC : Research in Information Systems and Technology
Publisher : RISTEC : Research in Information Systems and Technology

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

Abstract

XYZ University as an educational institution that is responsible not only for academic quality, but also for equal access to higher education, has established various scholarship programs. However, a fair and transparent selection process for scholarship recipients is still a challenge. Unclear criteria, poorly defined weights, and an unstructured decision-making process can result in injustice for prospective students who should be eligible for financial aid. To overcome this problem, it is necessary to develop a Decision Support System (SPK) which can assist in the selection process for underprivileged scholarship recipients. The Simple Additive Weighting (SAW) method is a method that can be implemented because it is relatively easy to implement and can provide transparent and accountable results. Based on the calculation results, the student with the highest calculated score, namely 1,400, is a student who is recommended to be entitled to an educational scholarship.
Co-Authors Agus Sunandar, Muhamad Angga Permana, Muhammad Arialdi, Miftahul Dede Irmayanti Gazali Mahmud, Firman Indra Pangestu, Panji Maruf Nugroho, Imam Muhyidin, Yusuf Raymond Ramadhan, Yudhi Syariful Alam