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All Journal Jiko (Jurnal Informatika dan komputer) Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Mantik Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurnal Sains dan Teknologi Jurnal Bangkit Indonesia STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer RISTEC : Research in Information Systems and Technology

Syariful Alam

STT Wastukancana

Author-ID : 5373851

Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Twitter User Sentiment Analysis Of TIX ID Applications Using Support Vector Machine Algorithm Asyfah Nabillah; Syariful Alam; Mochzen Gito Resmi
RISTEC : Research in Information Systems and Technology Vol 3, No 1 (2022): Research in Information Systems and Technology
Publisher : Institut Pendidikan Indonesia

The cinema is a place to watch movies using the big screen. Various comments on the TIX ID application service can be used to reference the company's evaluation material in assessing the level of service quality that has been provided, so that later the TIX ID application can be used optimally by application users and the company, as for the purpose of this study to find out responses and find out the sentiment analysis stage on twitter social media using the vector machine support algorithm for the TIX ID application. This algorithm is commonly used for text mining by going through the data collection stage, cleaning and labelling data stage, training and testing data sharing stage with 3 comparison scenarios, namely 70:30, 80:20, and 90:10 using 3 kernels, namely dot, radial, and polynomial, then through the text preprocessing stage, the TF-IDF word weighting stage, the data modeling stage, and the evaluation stage. The preprocessing stage consists of transform case, tokenize, and stopwords filters. The result of this study is that the support vector machine algorithm has an accuracy value of 74.17%. The research concludes that the support vector machine algorithm with a ratio of 80:20 training and testing data ratio scenario produces the highest accuracy.

Sentiment Analysis of Police Performance On Twitter Users Using Naïve Bayes Method Muhammad Hidayatullah; Syariful Alam; Irsan Jaelani
RISTEC : Research in Information Systems and Technology Vol 2, No 2 (2021): Research in Information Systems and Technology
Publisher : Institut Pendidikan Indonesia

After the case of alleged child rape in East Luwu which was stopped went viral in the aftermath of many other cases of sexual violence that were considered by the police to be inconsistent with procedures. After the hashtag #PercumaLaorPolisi appeared #PolriSesuaiProsedur hashtag became a trending topic on Twitter. This study discusses the sentiment of police performance on twitter users, aiming to measure how much sentiment the performance of the police according to twitter citizens who earned. The topic of this study is a mining text that uses the naïve bayes method. Text mining is a computer-based algorithmic technique/approach to gaining new knowledge hidden from a set of texts. The data from crwalling on twitter were analyzed using naive bayes which is a method for analyzing. Naive Bayes' algorithm is very effective in classification or classification problems. This algorithm works based on existing probabilities to determine the probability of the future. The steps in the Naïve Bayes method are preprocessing which includes transformation, tokenization and filtering processes. It is followed by the weighting of words such as TF-IDF and ends with classification and evaluation. As a result of this study, according to tweet data processed using the orange application and confusion matrix calculations, the police performance sentiment entered the neutral classification of 75.8%, negative 58.1% and positive 39.5% in the last order, as well as the resulting model at an accuracy value of 0.929, precision 0.933, recall 0.923, and f-measure 0.954

Implementation of the Multy Attribte Utily Theory Method in the Decision Support System for Determining Smart Indonesia Program Assistance (PIP) at SDN 4 Cisalada Fitry Nur Yani; Mochzen Gito Resmi; Syariful Alam
RISTEC : Research in Information Systems and Technology Vol 3, No 1 (2022): Research in Information Systems and Technology
Publisher : Institut Pendidikan Indonesia

The Smart Indonesia Program (PIP) is one of the government's programs as Poor Student Assistance (BSM). The government program is in the form of cash assistance given to children aged 6-21 years who are still in the world of education. Currently, pip selection at SDN 4 Cisalada from the school is less targeted to have problems in determining potential beneficiaries of assistance where not all students who come from poor families can receive the Smart Indonesia Program (PIP). for this resulting in the injustice of students who should be entitled to PIP funding assistance. To avoid existing problems for the selection of prospective recipients of the Smart Indonesia Program (PIP) requires a Decision Support System (SPK). The Multi Attribute Utility Theory (MAUT) method is a quantitative comparison method that usually combines measurements of different risk and profit costs. Design and Build using the Waterfall method in the process of working on it, the design is made using a flowmap and modeling using Unified Model Language (UML) including Use Case Diagrams, Activity Diagrams, Sequence Diagrams and Class Diagrams. As for programming, it uses PHP and the database uses MySQL. The results of the research that has been carried out by the researcher, it can be concluded by the application of the Decision Support System in determining the determination of PIP assistance in schools using the Multy Attribute Utility Theory (MAUT) method, for the school can be more objective in assessing the determination of PIP recipients, so as to minimize the risk of misuse and distribution of PIP funds to students not appropriately receiving them. Which is in the nature of providing recommendations for decisions to the school.

Co-Authors Alif Fajar Panjalu Anggi Puji Astuti Annisa Anggraeny Asyfah Nabillah Dede Irmayanti Dwi Fristtikasari Febriyanti Syofiani Fitry Nur Yani Fuad Amirullah Gilbert Imay Kurniawan Imay Kurniawan Irsan Jaelani Moch Saoki Syahlan Mochammad Imam Sulistyo Sarjowo Muhammad Hidayatullah Nunung Masripah Putri Apriliyanti Resmi, Mochzen Gito Salsabilla Choerunnisa Nurzanah Teguh Iman Hermanto Teguh Iman Hermanto Vina Alviani Yuni Nurtikasari

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