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All Journal Prosiding SNATIKA Vol 01 (2011) Telematika Jurnal CoreIT Applied Information System and Management Technomedia Journal Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science IJIIS: International Journal of Informatics and Information Systems Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia International Journal of Engineering, Science and Information Technology Jurnal Abdi Masyarakat Indonesia
Taqwa Hariguna
STMIK AMIKOM Purwokerto
Humanities Astronomy 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Isu Cloud Computing e-government di Indonesia 2014 Hariguna, Taqwa
Prosiding SNATIKA Vol 01 (2011) Vol 1
Publisher : Prosiding SNATIKA Vol 01 (2011)

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Abstract

Penelitian ini membahas tentang isu penerapan teknologi cloud computing yang akan di implementasikan pada pemerintahan baik di pusat atau di daerah (e-government) 2014, metodologi yang digunakan adalah Service Oriented Architectur (SOA). Dengan cloud computing diharapkan akan meminimalisir terjadinya kegagalan dalam penerapan e-government dan dapat menekan anggaran belanja infrastruktur TI/SI.   Kata Kunci : Cloud Computing, E-government, SOA
Pengukuran Perilaku Publik Terhadap Layanan E-Government Menggunakan Expectation Confirmation Model dan Kualitas Sistem Informasi Hariguna, Taqwa; Berlilana, Berlilana
Telematika Vol 12, No 1: Februari (2019)
Publisher : STMIK Amikom Purwokerto

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Abstract

Penelitian ini merupakan penelitian awal dalam membangun sebuah kerangka teori baru yaitu kualitas system informasi (information system quality), teori ini dielaborasi dengan teori expectation confirmation model, tujuan dari peneltian ini adalah untuk membangun sebuah konsep atau teori yang dapat digunakan pada layanan e-government khusunya diera industry 4.0. Hasil dari peneltian ini adalah terbentuknya variable baru yaitu credibility, reliability dan trust sebagai second order formative construct, dan terdapat tujuh hipotesis yang terbentuk.
Analisis Sentimen Pengguna Aplikasi Bukalapak di Platform Playstore Menggunakan Metode Naïve Bayes Subhan Mahendrasyah, Muhammad; Hariguna, Taqwa
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5528

Abstract

Indonesia, as a country with significant growth in internet users, recorded a 7.3% digital economy contribution to GDP in 2017, surpassing the overall economic growth of 5.1%. One of the main challenges is efficiency in managing user reviews to improve services, as done by Bukalapak app using data scraping to collect 5000 reviews. This study uses the Naïve Bayes algorithm to analyze the sentiment of Bukalapak app user reviews, focusing on identifying positive and negative sentiment patterns. The goal is to deepen the understanding of user perceptions of Bukalapak services and provide a basis for strategic decision-making in improving user experience and application services. The Naïve Bayes algorithm in this study achieved an accuracy rate of 67.9%, with 13.3% of reviews found to be positive and 86.7% of reviews negative. The analysis results highlight the importance of improvements in certain aspects of Bukalapak's services, which can lead to further development to increase user satisfaction. The majority of Bukalapak reviews indicate shortcomings or criticism of its services, which highlights the importance of improvement in certain aspects. The Naïve Bayes model provides a clear understanding of user sentiment, which is key in strategic decision-making and efforts to improve user experience on the Bukalapak platform. Thus, this research makes an important contribution in directing further improvement and development steps in enhancing Bukalapak app services as well as better understanding user perceptions.
PELATIHAN PENULISAN KARYA ILMIAH STANDAR INTERNATIONAL PADA DOSEN UNIVERSITAS AMIKOM PURWOKERTO Hariguna, Taqwa; Waluyo, Retno; Muflikhatun, Siti; Ramadani, Nevita Cahaya
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 4 No. 6 (2023): Volume 4 Nomor 6 Tahun 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v4i6.22327

Abstract

Melaksanakan penelitian merupakan tugas seorang dosen. Dosen melaksanakan penelitian memiliki tugas mengembangkan suatu cabang ilmu pengetahuan dan/atau teknologi melalui penalaran dan penelitian ilmiah serta menyebarluaskannya baik secara perseorangan maupun berkelompok. Hasil karya ilmiah dosen dapat dipublikasikan dalam sebuah jurnal atau prosiding yang bereputasi nasional ataupun internasional. Berdasarkan data SINTA tahun 2023 rangking Universitas Amikom Purwokerto menempati ranking ke 302. Pada 3 tahun terakhir jumlah dokumen yang terindeks scopus mengalami penurunan. Hal ini dikarenakan sebagian besar dosen belum terbiasa untuk menulis karya ilmiah dengan standar internasional dan pengetahuan serta kemampuan dosen di bidang penulisan dan publikasi karya ilmiah belum mencukupi untuk menulis jurnal dengan standar internasional. Oleh karena itu kegiatan pengabdian masyarkat yang ditawarkan dengan memberikan pelatihan penulisan karya ilmiah pada dosen Universitas Amikom Purwokerto. Dengan tujuan meningkatkan kemampuan dosen membuat karya ilmiah, sehingga kualitas karya ilmiah yang dibuat sesuai standar internasional. Hasil kegiatan ini berjalan dengan lancar dan sebanyak 80% peserta mendapatkan pengetahuan baru penulisan artikel ilmiah yang sesuai dengan standar international.
Sentiment Analysis of Product Reviews as A Customer Recommendation Using the Naive Bayes Classifier Algorithm Hariguna, Taqwa; Baihaqi, Wiga Maulana; Nurwanti, Aulia
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.13

Abstract

In an e-commerce Shopee, the process of selling and buying continues to run every day, and the comments given by consumers will increase more and more. Comments given by consumers will be the reference/review of a product that has been purchased by consumers. Consumers freely provide a review containing positive comments and negative comments in the Comments field listed on the Shopee e-commerce website. With the above problems, researchers will do a research with the method of sentiment analysis to distinguish classes in product review comments that include positive comment class or negative comment class using a combination of K-means and naive Bayes classifier. K-means used to determine the grouping of classes; naive Bayes classifier used to get the value of accuracy. The results obtained based on clustering K-means include getting 116 negative comments on product reviews and 37 negative comments product reviews. Accuracy results obtained from product review comment data of 77.12%. Thus, the accuracy value using K-means and naive Bayes classifier without manual data get a higher accuracy value is compared using K-means, Naive Bayes classifier, and manual data get results lower accuracy of 56.86%. From the results above the most comments is a negative comment of 116 data review comments product, from the results of the study can be concluded that one of the products of Spatuafa named high heels women know the Ribbon Ikat FX18 the condition of the product is not good enough due to the high negative comments compared to positive comments
COVID-19 Vaccination: A Retrospective Observation and Sentiment Analysis of the Twitter Social Media Platform in Indonesia Hananto, Andhika Rafi; Rahayu, Silvia Anggun; Hariguna, Taqwa
International Journal of Informatics and Information Systems Vol 5, No 1: January 2022
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v5i1.126

Abstract

Coronavirus (COVID-19) is a rapidly emerging and spreading infectious disease. To minimize the impact caused by the virus, it is necessary to have a vaccine. However, the existence of vaccinations for the Indonesian people has caused controversy so that it invites many people to give an opinion assessment, therefore people choose social media as a place to channel their opinions. In this study, a comparison was made with an observational infoveillance study by collecting data using a Python programming script (Python Software Foundation) to display posts related to the COVID-19 vaccine on Twitter as well as quantitative and qualitative analysis to identify trends and characterize the main themes discussed by twitter users on Twitter. Indonesia. Our research collects data through social media Twitter in the period August 2020 - March 2021. In this study we combine Retrospective Observation and Sentiment Analysis, with the aim of producing periodic timeline evaluations within a predetermined time frame. In this study author found that there was an interaction increase in positive posts due to officially reported developments, on the other hand we were quite difficult to understand the factors behind the emergence of negative posts but we made a conclusion based on the results of sentiment analysis that most of the negative posts were caused by lack of information and understanding of vaccines and vaccines. the COVID-19 outbreak itself.
Sales Transaction Data Analysis using Apriori Algorithm to Determine the Layout of the Goods Hariguna, Taqwa; Hasanah, Uswatun; Susanti, Nindi Nofi
International Journal of Informatics and Information Systems Vol 1, No 1: September 2018
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v1i1.19

Abstract

In a shop, usually apply a sales strategy in order. The sales strategy can be in the form of determining the layout of goods so that they are close to one another. Determining the layout of items can be based on items that are often purchased simultaneously. Searching for items that are often purchased together can be done using data mining techniques, which is processing data to become more useful information. Sales transaction data processing can be done using apriori algorithm. Apriori algorithm is the most famous algorithm for finding high-frequency patterns and generating association rules. From the results of the discussion and data analysis, there were 3 (three) association rules formed, namely "If you buy Milo Active 18 grm, then buy ABC Kopi Susu 31G" with support 0.36% and 75% confidence, "If you buy Dancow 1 + Honey 200 grm, then buy Ice Cream Corneto" wit H Support 0.36% and confidence 60%, "If you buy SIIP Roasted 6.5 grm, then buy Davos Strong 10 grm" with support 0.36% and 75% confidence. From the association's rules can be used as decision making to determine the layout of goods that are likely to be purchased simultaneously by the buyer
Integrating Big Data Technologies to Strengthen Network Security Awareness Hariguna, Taqwa
International Journal of Informatics and Information Systems Vol 5, No 3: September 2022
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v5i3.137

Abstract

The Network Security Alarm Warning System (NSAWS) is a sophisticated, real-time, large-scale database management system designed to enhance cybersecurity by analyzing user identity data and access rights within the amassed information to detect potential threats and issue timely alarm notifications. This paper explores methods to bolster network attack prevention in a Big Data framework, starting with an overview of prevalent internet technologies in our country and their current applications. It then elaborates on the architecture, deployment strategies, and operational methodologies of the NSAWS, emphasizing the integration of fundamental security infrastructures like cloud computing platforms and firewalls. Following this, the traditional NSAWS is analyzed, and a simulation platform is tested to evaluate its performance in identifying and alerting network security threats. The results indicate that the NSAWS platform demonstrates exceptional accuracy and stability in its warning capabilities, effectively safeguarding network security by swiftly addressing potential vulnerabilities and threats. This paper underscores the importance of leveraging advanced technologies and robust security frameworks to fortify network defenses against evolving cyber threats, highlighting the NSAWS as a vital tool in maintaining and enhancing network security in an increasingly digital and interconnected world.
An Ensemble and Filtering-Based System for Predicting Educational Data Mining Hananto, Andhika Rafi; Rahayu, Silvia Anggun; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 2, No 4: DECEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i4.44

Abstract

When developing a prediction paradigm, an ensemble technique such as boosting is used. It is built on a heuristic framework. Generally speaking, engineering ensemble learning is more accurate than individual classifiers when it comes to making predictions. Consequently, numerous ensemble strategies have been presented in this work, particularly to provide a more complete understanding of the essential methods in general. Researchers have experimented with boosting methods to forecast student performance as part of a variety of ensemble techniques. The researchers employed improvement approaches to construct an accurate predictive educational model, which was based on a key phenomena seen in categorization and prediction operations. In light of the uniqueness and originality of the suggested strategy in educational data mining, the researchers used augmentation strategies in order to construct an accurate predictive pedagogical model. Tenfold cross-validation was performed to evaluate the effectiveness of the basic classifiers, which included the random tree, the j48, the knn, and the Naive Bayes. The random tree was found to be the most effective classifier. Several additional screening techniques, including oversampling (SMOTE) and undersampling (Spread subsampling), were utilized to analyze any statistically significant variations in results between the meta and base classifiers that had been identified between the meta and base classifiers. The use of ensemble and screening strategies, as compared to the use of standard classifiers, has demonstrated considerable gains in predicting student performance, as has the use of either strategy alone. Furthermore, after the completion of a performance research on each approach, two new prediction models have been established on the basis of the improved results gained thus far.
Market Basket Analysis Using FP-Growth Algorithm to Design Marketing Strategy by Determining Consumer Purchasing Patterns Saputra, Jeffri Prayitno Bangkit; Rahayu, Silvia Anggun; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 4, No 1: JANUARY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i1.83

Abstract

The development and competition that exists in the business world today leads every manager or company to be more dexterous in making marketing strategies to increase sales. Various things are done to keep up with existing market competition, such as analyzing customer purchase transaction data to serve as a policy determination and decision-making system in making marketing strategies. In determining marketing strategies, it can be done by taking transaction data to see existing purchase or transaction patterns. Market Basket Analysis is part of a data mining method that uses the FP-Growth algorithm technique to find out associated products. This research uses data taken from sales transaction data archives as much as 150 sales transaction data and 26 product data. In this study, it is determined that the minimum support value is 50% and the minimum confidence is ≥ 0.75 From the test results, 9 products have superior support values and meet the minimum value. From the test results, a rule with a confidence value of 0.870 was obtained: D → W (if consumers buy Wardah Lightening Gentle Wash, then buy Azarine Sunscreen SPF50), and 0.808: A → E → O (if consumers buy Emina Face Wash, then buy Azarine Night Moisturizer and Himalaya Neem Mask).
Co-Authors Abbas, Intesar Abdul Azis Abdul Hakim Prima Yuniarto Abidin, Uun Anton Satria Prabuwono Azhari Shouni Barkah Baihaqi, Wiga Maulana Bangkit, Jeffri Prayitno Berlilana Berlilana Chen, Shih-Chih Cheng, Tsang-Hsiang Dhanar Intan Surya Saputra Eko Winarto El Emary, Ibrahiem M. M. Fitriani, Radifa Rahma Giat Karyono Hadie, Agus Nur Hananto, Andhika Rafi Harahap, Eka Purnama Hidayat, Muhammad Taufik Nur Husni Teja Sukmana Hussain, Refat Taleb Imam Tahyudin Jawad, Haider Mahmood Johan Reimon Batmetan Kim, Jong Il Mohammed, Ismail Abdulaziz Muflikhatun, Siti Muhamad Yusup NAWANGNUGRAENI, DEVI ASTRI Nazwan, Nazwan Nurwanti, Aulia Pratama, Satrya Fajri Priyadi, Agung PUJI LESTARI Qurotul Aini R Rizal Isnanto Rahayu, Silvia Anggun Ramadani, Nevita Cahaya Riawan, Fendi Rozak, Rofik Abdul Ruangkanjanases, Athapol Sabah, Sura Salsabila, Salsabila Sarmini Sarmini Subhan Mahendrasyah, Muhammad Susanti, Nindi Nofi Tikaningsih, Ades Uswatun Hasanah Wahid, Arif Mu'amar Waluyo, Retno Yarsasi, Sri Yi, Ding