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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat CARADDE: Jurnal Pengabdian Kepada Masyarakat IJAIT (International Journal of Applied Information Technology) Prosiding Konferensi Nasional PKM-CSR Indonesian Community Journal Literasi: Jurnal Pengabdian Masyarakat dan Inovasi eProceedings of Applied Science eProceedings of Engineering Jurnal Pengabdian Masyarakat Bangsa
Dedy Rahman Wijaya
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : IJAIT (International Journal of Applied Information Technology)

 1 
Combination of Analytic Hierarchy Process and Simple Additive Weighting for Tourist Attractions Recommendation System Elis Hernawati; Siska Komala Sari; Dedy Rahman Wijaya
IJAIT (International Journal of Applied Information Technology) Vol 05 No 02 (November 2021)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v5i02.4472

Abstract

The selection of the right tourist attractions is always done by tourists before visiting tourist attractions. Tourists have different criteria in choosing the tourist attractions they want to visit. There are many good tourist attractions on the island of Lombok, Indonesia, but of the many tourist attractions, tourists need recommendations for the best tourist attractions to visit. Decision-making methods can be used to create a ranking system. Analytical Hierarchical Process (AHP) is a decision-making method in Multi-Criteria Decision Making (MCDM) problems by combining qualitative and quantitative factors in complex problems. Simple Additive Weighting (SAW) is a decision-making method to generate a rating preference value. The purpose of this paper is to utilize a combination of AHP-SAW to decide the weight of the criteria and the significance of alternative tourist attractions. The results of the calculation of the AHP-SAW combination resulted in the ranking of the best tourist attractions. This study uses five alternative tourist attractions on the island of Lombok, namely Pink Beach (A1), Senggigi Beach (A2), Tanjung Aan Beach (A3), Marese Hill (A4), and Mayura Park (A5) taken fro m several trusted sites. In addition, the five criteria used are visitor reviews, visitor ratings, ticket prices, the distance of tourist attractions fro m the airport, and visiting time. The results showed that the AHP-SAW combination resulted in a consistent ratio value of 0.0371 so that the criterion-weighted data could be used as a basis for calculating the preference value and ranking of alternative tourist attractions. The best alternative for tourist attractions is Tanjung Aan Beach (A3) with a preference value of 0.9554.
Simple Machine Learning Architecture as a Service Tora Fahrudin; Dedy Rahman Wijaya
IJAIT (International Journal of Applied Information Technology) Vol 07 No 01 (May 2023)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v7i01.5991

Abstract

Machine learning (ML) development starting in the 1950s, has shown significant progress. Various fields have used machine learning as an information system element that is useful in assisting data processing, personalization, prediction, and performing anomaly detection of occurring transactions. Along with developments, cloud-based machine learning technology is becoming the choice for ease of implementation and connectivity with various other technology platforms. This paper proposes a machine learning architecture as a service (MLaaS) implemented in a case study of a gender prediction model based on height and weight. The results show that the MLaaS architecture is straightforward to implement and fits the needs of various access environments and the ease of updating models centrally. Our gender prediction model achieved 91.78% in the precision, recall, and F1-score, 91.8% in specificity and NPV, and 91.79% in accuracy.
Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection Pramuko Aji; Dedy Rahman Wijaya; Elis Hernawati; Sherla Yualinda; Sherli Yualinda; Muhammad Akbar Haikal Frasanta; Rathimala Kannan
IJAIT (International Journal of Applied Information Technology) Vol 7 No 02 (2023): Vol 07 No 02 (November 2023)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v7i02.5374

Abstract

The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.
Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection Aji, Pramuko; Wijaya, Dedy Rahman; Hernawati, Elis; Yualinda, Sherla; Yualinda, Sherli; Frasanta, Muhammad Akbar Haikal; Kannan, Rathimala
IJAIT (International Journal of Applied Information Technology) Vol 07 No 02 (November 2023)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v7i02.5374

Abstract

The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.
Co-Authors , Inne Gartina Aditya Permadi, Aditya Aditya, Bayu R Aldilla Iradianty Ardiansyah, Fajri Ariyani, Fathya Asniar Asniar Astri Wulandari Bayu Rima Aditya Bethani Suryawardani Damanik, Junia Putri Dendi Gusnadi Elis Hernawati Fazri, Irfan Moch Frasanta, Muhammad Akbar Haikal Gartina, Inne Gunadi, Gagah Aji Gunawan, Tedi Guntur Prabawa Kusuma Hermawan, Laksamana Mikhail Heru Nugroho Inne Gartina Irfan Moch Fazri Kannan, Rathimala Ledya Novamizanti Marcelino, Dandy Marchiningrum, Anranur Uwaisy Muhammad Akbar Haikal Frasanta Muhammad Nezar Mahardika Muhammad, Rafi Narwin, Agung Isra Pramuko Aji Pratama , Nyoman Raflly Priambodo, Hanang Raswyshnoe Boing Kotjoprayudi Rathimala Kannan Riyanarto Sarno Rosyadi, Mohammad As'ad Setyagraha , Muhammad Rafi Mahfuz Sherla Yualinda Sherli Yualinda Siska Komala Sari Sri Rahayu SRI RAHAYU Suryana Suryana Suryatiningsih Tora Fahrudin Wawa Wikusna Widyantari Febiyanti Yualinda, Sherla Yualinda, Sherli