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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
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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Classification of Covid-19 vaccine data screening with Naive Bayes algorithm using Knowledge Discovery in database method Syariful Alam; Mochzen Gito Resmi; Nunung Masripah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i2.1584

Abstract

Acute Respiratory Syndrome Coronavirus-2 (SARS-Cov-2) known as covid-19 was detected and caused a very large number of deaths due to a mysterious respiratory disease. With the death toll continuing to rise, the government was forced to take swift action to break the chain of spread and reduce the number of deaths by taking vaccinations. An adequate vaccine against Covid-19 is expected to vaccinate at least 70% of the population. Therefore, this study was carried out as a step to help break the chain of the spread of the Covid-19 virus, by classifying the Covid-19 vaccine screening data. The research method applied in this study is the Knowledge Discovery in Database (KDD) method, in which there are several processes, namely selection, pre-processing, transformation, data mining, and evaluation. The application of the Naive Bayes method is expected to be able to classify Covid-19 vaccine screening data with vaccine class values, no, and delay. The results of the research on the classification of the Naive Bayes method show that there are 959 data with Vaccine data 695, No 200, and Delay 64. Processed using the Rapidminer application, the accuracy is 96.56%, Precision is 92.46%, and Recall is 92.13%.
ANALISIS ASSOCIATION RULE UNTUK IDENTIFIKASI POLA GEJALA PENYAKIT HIPERTENSI MENGGUNAKAN ALGORITMA APRIORI (STUDI KASUS: KLINIK RAFINA MEDICAL CENTER) Salsabilla Choerunnisa Nurzanah; Syariful Alam; Teguh Iman Hermanto
Jurnal Informatika dan Komputer Vol 5, No 2 (2022)
Publisher : JIKO (Jurnal Informatika dan Komputer)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v5i2.4792

Abstract

Hipertensi didefinisikan sebagai penyakit krnis yang dapat menyebabkan tekanan darah meningkat melebihi 140/90 mmHg. Tidak adanya gejala spesifik dari penyakit hipertensi dapat menyebabkan penyakit yang serius atau komplikasi pada penderita hipertensi, oleh karena itu hipertensi dapat dilakukan sebagai “silent killer”. Klinik Rafina Medical Center merupakan suatu fasilitas kesehatan tingkat pertama di Kabupaten Purwakarta, Jawa Barat. Di Klinik Rafina Medical Center sendiri penyakit hipertensi dari tahun 2020 sampai awal tahun 2022 selalu termasuk ke dalam 10 diagnosa terbanyak, artinya banyak pasien yang terdiagnosa hipertensi di klinik tersebut. Oleh karena itu, dilakukan identifikasi menggunakan metode data mining yang bertujuan untuk menghasilkan informasi mengenai pola gejala hipertensi. Metode analisis data pada penelitian ini yaitu metode Knowledge Discovery in Database (KDD). Metode data mining yang digunakan yaitu metode association rule, dimana metode ini dapat mencari dan mengidentifikasi pola asosiasi antar atribut dalam suatu dataset. Algoritma pada penelitian ini adalah algoritma apriori. Tools dalam penelitian ini menggunakan software Orange. Hasil dari penelitian ini berupa aturan asosiasi atau pola gejala hipertensi, sehingga dari hasil tersebut diharapkan dapat memberikan pengetahuan baru mengenai pola gejala hipertensi sehingga dapat digunakan dalam penyuluhan dan penyediaan obat bagi penderita hipertensi khususnya di Klinik Rafina Medical Center.
Komparasi Algoritma Support Vector Machine dengan Naive Bayes Untuk Analisis Sentimen Pada Aplikasi BRImo Anggi Puji Astuti; Syariful Alam; Irsan Jaelani
Jurnal Bangkit Indonesia Vol 11 No 2 (2022): Bulan Oktober 2022
Publisher : LPPM Sekolah Tinggi Teknologi Indonesia Tanjung Pinang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (491.277 KB) | DOI: 10.52771/bangkitindonesia.v11i2.196

Abstract

Banking is an industry that is currently developing in the use of information technology by increasing service quality standards in order to compete in the market in an increasingly tight digital era. At this time, BRI bank is attracting public attention to the quality of renewal by launching a mobile banking application, therefore an analysis of reviews of BRImo mobile banking users is carried out to serve as an object of research by comparing text mining classification methods. This study aims to determine the results of the comparison of the Support vector machine and Naive Bayes algorithms. Support vector machine and Naive Bayes algorithm are classification methods used to process data in the form of text with a good level of accuracy. This algorithm is usually used for text mining analysis with 4 stages, namely data scrapping, preprocessing, classification and evaluation. At the preprocessing stage, there are several processes including filtering, labeling, case folding, tokenization, stopword removal & stemming and normalization to get relevant words to be classified. The results of this study are the Support vector machine algorithm has a better performance in classifying the BRImo mobile banking application review data with an accuracy value of 97,69% compared to the Naive Baye algorithm with an accuracy value of 96,53%.
Designing a mobile sales application at Setra farma pharmacy in the era of the covid-19 using the goal-directed desing method Annisa Anggraeny; Mochzen Gito Resmi; Syariful Alam
Jurnal Mantik Vol. 6 No. 3 (2022): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i3.2780

Abstract

Setra Farma Pharmacy is a company that handles product and servicesn by providing services in the form of pharmaceutical servies. Setra Farma pharmacies like the accumulation of queues of buyers and the absence of queue numbers especially in Covid-19 is very prone to infection. With this, Apotek Setra Farma wants to develop a mobile-based sales application to assist patients in finding and ordering drugs. This study uses the Goal-Directed Design method with prorotype testing using the single ease question method. When testing the user will be given a task that is rated on a 1-7 point linkert scale. The result of this research is the user interface application mobile that can be used at the Setra Farma Pharmacy. The final results obtained after testing the prototype with 5 users showed a score of 6.6 on the linkert scale of 1-7 points,which means the application is easy for users to use.
Analisis Sentimen Opini Masyarakat Terhadap Film Pada Platform Twitter Menggunakan Algoritma Naive Bayes Yuni Nurtikasari; Syariful Alam; Teguh Iman Hermanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 1 No. 4 (2022): Agustus 2022
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v1i4.770

Abstract

Film is one of the most interesting topics to talk about. When someone writes an opinion on a film, all the elements in the film will be written down. Film opinion data in this study were taken from film comments written on twitter. The number of opinions written on Twitter requires classification according to the sentiments they have so that it is easy to get the tendency of the opinion towards the film whether it tends to have a positive, negative or neutral opinion. Recently, the spotlight on twitter media in Indonesia is a film with the title Horror-Ngeri Sedap. Ngeri-Ngeri Sedap is a 2022 Indonesian comedy-drama film directed and written by Bene Dion Rajagukguk. The film is set in the Batak Tribe, starring Arswendy Beningswara Nasution, Tika Panggabean, Boris Bokir Manullang, Gita Bhebhita Butar-butar, Lolox, and Indra Jegel. This causes differences in the views and opinions of twitter users towards the Horror Sedap film. So it is necessary to have a sentiment classification for the opinion. The use of the Naïve Bayes Algorithm was chosen in the analysis because it has the highest probability or opportunity value for data classification. Labeling on Twitter data is done manually by giving positive, negative, neutral sentiments to the raw dataset in Microsoft excel then the data enters the preprocessing transformation, tokenization and filtering stages. The tf-idf weighting is carried out when the data is complete in the transformation process, tf-idf is used to determine the number of occurrences of words, then the data classification is carried out using the Naïve Bayes Algorithm. confusion matrix testing is done after the data classification is complete using the orange tools. Based on the test results with the confusion matrix with orange tools, the average accuracy value is 0.65% and the precision value is 0.67%, and recall is 0.65%, and the percentage is neutral 0.83% in its classification. This proves that the public sentiment on the twitter platform towards the case of the film Horror Sedap is neutral and the Naïve Bayes Algorithm is considered reliable and valid in data processing.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (443.555 KB) | DOI: 10.31980/ristec.v3i1.1898

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (500.218 KB) | DOI: 10.31980/ristec.v2i2.1945

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (412.842 KB) | DOI: 10.31980/ristec.v3i1.1901

Abstract

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.
Analisis Sentimen Terhadap Kinerja KPU Menjelang Pemilu 2024 Berdasarkan Opini Twitter Menggunakan Naïve Bayes Fuad Amirullah; Syariful Alam; M.Imam Sulistyo S
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 2 No. 3 (2023): Agustus
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v2i3.2293

Abstract

Era globalisasi saat ini memengaruhi pesatnya kemajuan teknologi informasi yang beredar di masyarakat salah satu perantaranya ialah melalui media sosial Twitter, Twitter dapat dimanfaatkan sebagai sarana menyampaikan pendapat terkait saran, kritik, maupun opini-opini publik. Salah satunya perihal kinerja KPU menjelang pemilu 2024 yang muncul diakhir tahun 2022 dan awal tahun 2023, mengenai skandal manipulasi data keanggotaan parpol di website KPU, hingga penundaan pemilu. Penelitian ini menggunakan metode text mining yang terdiri dari tahapan scraping data, labeling, cleansing, preprocessing, algoritma Naïve Bayes, pembobotan memakai perhitungan TF-IDF, pengujian data memakai confusion matrix dengan menggunakan pemrograman python. Didapatkan data yang berjumlah 2475 data, yaitu sebanyak 321 data positif, 409 data netral, dan 1745 data negatif. Berdasarkan hasil pengujian confusion matrix dengan pemrograman python didapatkan hasil akurasi negatif sebesar 74.5% berdasarkan nilai tersebut membuktikan tingkat kepercayan pada kinerja KPU menjelang proses pemilu 2024 mendatang tergolong kurang baik.
ANALISIS SENTIMEN BERDASARKAN ULASAN PENGGUNA APLIKASI MYPERTAMINA PADA GOOGLE PLAYSTORE MENGGUNAKAN METODE NAÏVE BAYES Gilbert; Syariful Alam; M. Imam Sulistyo
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 2 No. 3 (2023): Agustus
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v2i3.2333

Abstract

Pada aplikasi MyPertamina peneliti menemukan banyak ulasan tidak sesuai dengan rating yang diberikan. Pada kasus ini juga peneliti membaca beberapa artikel / berita tentang pro dan kontra nya aplikasi MyPertamina dalam beberapa hal, Maka dari itu harus dilakukan analisis sentimen untuk mengetahui apakah ulasan pemakai aplikasi MyPertamina itu lebih banyak arah positif atau negatif. Penelitian ini menggunakan metode text mining yang terdiri dari tahapan scraping data, labelling, cleaning, preprocessing (transformation, tokenization, filtering). Untuk algoritma yang digunakan yaitu naïve bayes karena mempunyai nilai probabilitas atau peluang tertinggi untuk pengklasifikasian data, untuk pembobotan menggunakan perhitungan TF-IDF, dan pengujian data menggunakan confusion matrix dan visualisasi menggunakan wordcloud. Hasil penelitian ini mengenai analisis sentimen ulasan pengguna MyPertamina pada Google PlayStore berjumlah 3948 data, disimpulkan bahwa ulasan pengguna MyPertamina tergolong negatif dengan hasil presentase 91% nilai akurasi, 92% nilai precision, dan 100% recall nya.
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