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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi International Journal of Advances in Intelligent Informatics International Journal of Artificial Intelligence and Robotics (IJAIR) International Journal of Health Science and Technology
Agus Sasmito Aribowo
Universitas Pembangunan Nasional Veteran Yogyakarta
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Health Professions Nursing Public Health

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TEXT MINING UNTUK MENDETEKSI PLAGIASI DOKUMEN DENGAN PENERAPAN STEMMING NAZIEF-ADRIANI DAN ALGORITMA SMITH-WATERMAN Alvika Meitaningsih; Agus Sasmito Aribowo; Nur Heri Cahyana
Telematika Vol 17, No 2 (2020): Edisi Oktober 2020
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v1i1.3377

Abstract

Plagiarisme adalah tindakan menjiplak karya orang lain dan mengakui sebagai hasil karya pribadinya. Saat ini sudah banyak algoritma yang membahas cara mendeteksi plagiarisme dokumen teks seperti Cosine, Smith Waterman. Hasil penelitian sebelumnya menyatakan bahwa algoritma Smith Waterman memiliki keakurasian yang rendah, sehingga pada penelitian ini dilakukan pengembangan dari Algoritma Smith Waterman. Algortima Smith Waterman biasa digunakan didalam bidang bioinformatika untuk menentukan kesamaan DNA, akan tetapi dalam penelitian ini Algoritma Smith Waterman dapat diimplementasikan untuk mendeteksi dokumen. Proses pendeteksian kemiripan dokumen pertama-tama dilakukan proses preprocessing untuk menghilangkan imbuhan guna memudahkan proses pendeteksian dokumen yaitu dengan menggunakan stemming. Stemming yang digunakan dalam penelitian ini adalah Stemming Nazief & Adriani dan untuk mengukur tingkat keakurasian pada proses pendeteksian dokumen dilakukan perhitungan menggunakan algoritma Smith Waterman untuk mendapatkan hasil persentase kemiripan antar dokumen. Dari uji coba yang dilakukan penambahan preprocessing yaitu stemming mempengaruhi waktu proses pengujian karena pada proses preprocessing ini kata yang berimbuhan akan dikembalikan ke kata dasar dan dicocokan dengan data kamus yang ada didalam database.
Feasibility study for banking loan using association rule mining classifier Agus Sasmito Aribowo; Nur Heri Cahyana
International Journal of Advances in Intelligent Informatics Vol 1, No 1 (2015): March 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v1i1.8

Abstract

The problem of bad loans in the koperasi can be reduced if the koperasi can detect whether member can complete the mortgage debt or decline. The method used for identify characteristic patterns of prospective lenders in this study, called Association Rule Mining Classifier. Pattern of credit member will be converted into knowledge and used to classify other creditors. Classification process would separate creditors into two groups: good credit and bad credit groups. Research using prototyping for implementing the design into an application using programming language and development tool. The process of association rule mining using Weighted Itemset Tidset (WIT)–tree methods. The results shown that the method can predict the prospective customer credit. Training data set using 120 customers who already know their credit history. Data test used 61 customers who apply for credit. The results concluded that 42 customers will be paying off their loans and 19 clients are decline
Effects of attractiveness, image and satisfaction on word of mouth communication Dyah Sugandini; Mohamad Irhas Effendi; Yenni Sri Utami; Agus Sasmito Aribowo
International Journal of Health Science and Technology Vol 2, No 1 (2020): July
Publisher : Universitas 'Aisyiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (246.445 KB) | DOI: 10.31101/ijhst.v2i1.1818

Abstract

Travel activities have become the lifestyle of today's society. Many people take advantage of a holiday just to travel to various tourist attractions. In the context of tourism, lifestyles are also associated with activities, hobbies, opinions, which play an important role in consumer behavior. Trend tours that became a lifestyle one of them is special interest tourism. Dolandeso is one of the special interest attractions with environmental conservation mission based on cultural values and local wisdom. Dolandeso is a community-run tourism and is a Community based Tourism (CBT). Dolandeso tourism is a blend of the beauty of the environment, nature and value of harmonization between humans. This study aims to analyze the Word of Mouth (WOM) model influenced by satisfaction, image and attractiveness. This research uses 100 international tourist respondents. The sampling technique uses convience sampling. Data analysis using two step approach to SEM. The results show that the WOM model is acceptable. Satisfaction has positive effect on WOM, image has positive effect on attractiveness and satisfaction, attractiveness has positive effect on satisfaction.
Semi-supervised Learning Models for Sentiment Analysis on Marketplace Dataset Wisnalmawati Wisnalmawati; Agus Sasmito Aribowo; Yunie Herawati
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 4 No. 2 (2022): November 2022
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (404.442 KB) | DOI: 10.25139/ijair.v4i2.5267

Abstract

Sentiment analysis aims to categorize opinions using an annotated corpus to train the model. However, building a high-quality, fully annotated corpus takes a lot of effort, time, and expense. The semi-supervised learning technique efficiently adds training data automatically from unlabeled data. The labeling process, which requires human expertise and requires time, can be helped by an SSL approach. This study aims to develop an SSL-Model for sentiment analysis and to compare the learning capabilities of Naive Bayes (NB) and Random Forest (RF) in the SSL. Our model attempts to annotate opinion documents in Indonesian. We use an ensemble multi-classifier that works on unigrams, bigrams, and trigrams vectors. Our model test uses a marketplace dataset containing rating comments scrapping from Shopee for smartphone products in the Indonesian Language. The research started with data preparation, vectorization using TF-IDF, feature extraction, modeling using Random Forest (RF) and Naïve Bayes (NB), and evaluation using Accuracy and F1-score. The performance of the NB model outperformed previous research, increasing by 5,5%. The conclusion is that SSL performance highly depends on the number of training data and the compatibility of the features or patterns in the document with machine learning. On our marketplace dataset, better to use Random Forest.
Co-Authors Alvika Meitaningsih Dyah Sugandini Muhammad Irhas Effendi, Muhammad Irhas Nur Heri Cahyana Wisnalmawati Wisnalmawati Yenni Sri Utami Yunie Herawati