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All Journal Techno.Com: Jurnal Teknologi Informasi Pixel : Jurnal Ilmiah Komputer Grafis SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputer Terapan CogITo Smart Journal Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK Polbeng - Seri Informatika JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL INSTEK (Informatika Sains dan Teknologi) ILKOM Jurnal Ilmiah INTECOMS: Journal of Information Technology and Computer Science Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) CSRID (Computer Science Research and Its Development Journal) JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Informatika dan Rekayasa Elektronik Zonasi: Jurnal Sistem Informasi JSR : Jaringan Sistem Informasi Robotik Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal J-PEMAS Jurnal Dinamika Informatika (JDI) Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) sudo Jurnal Teknik Informatika Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Algoritma JAIA - Journal of Artificial Intelligence and Applications Jurnal Komtekinfo Malcom: Indonesian Journal of Machine Learning and Computer Science DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Innovative: Journal Of Social Science Research SATIN - Sains dan Teknologi Informasi VISA: Journal of Vision and Ideas Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika
Rahmaddeni Rahmaddeni
STMIK Amik Riau
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Penerapan Algoritma Naïve Bayes Untuk Pengelompokkan Predikat Peserta Uji Kemahiran Berbahasa Indonesia Michal Dennis; Rahmaddeni Rahmaddeni; Fransiskus Zoromi; M. Khairul Anam
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i2.3956

Abstract

Indonesian Language Proficiency Test is a proficiency test to measure a person's language proficiency in communicating using Indonesian, both Indonesian speakers and foreign speakers. The UKBI rating has 7 rating categories consisting of special, very excellent, excellent, intermediate, poor, marginal, and limited. The number of participants who take UKBI at the Riau Province Language Center has more than 1000 but no one has managed the data into new knowledge. One of the efforts that can be done with the data is classification. The Naïve Bayes Classification Algorithm is a classification algorithm that is very effective (getting the right results) and efficient (the reasoning process is carried out by utilizing existing inputs in a relatively fast way). In order to obtain good accuracy results, the Naive Bayes Algorithm is combined with the Adaboost feature selection with a 70:30 and 80:20 test scheme. The results of the research carried out resulted in the highest accuracy value, namely 89% which combined the Naive Bayes algorithm with the Adaboost feature selection with 70:30 data splitting
Prediksi Penambahan Piutang Iuran Jaminan Sosial Ketenagakerjaan menggunakan Algoritma K-Nearest Neighbor Devi Efriadi; Rahmaddeni Rahmaddeni; Agustin Agustin; Junadhi Junadhi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 6, No 1 (2022): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v6i1.5255

Abstract

There are several issues with Social Security Organizing Agency (BPJS) employment at the moment, one of which is contribution receivable. To reduce the BPJS contribution receivables, BPJS has done various ways. However, the resulting effort is not maximal enough to reduce the number of receivables in BPJS. This study aims to provide input by predicting the addition of receivables from social security contributions made by several companies or organizations. This study used the K-Nearest Neighbor (KNN) Algorithm with a cross-validation technique. KNN is a very simple classification method in classifying an image based on the closest distance to its neighbors. This study conducted data processing from BPJS use, which amounted to 1193 data. The data is then preprocessed so that the processed data is clean from missing and noise, this data uses 70:30 data splitting. After the preprocessing and splitting of data were carried out, the next step was to do modeling using KNN, so the cross-validation to improve the accuracy of results obtained from the KNN algorithm. The results obtained from this research get the highest accuracy of 92% with the Optimal K value being 6, then the ROC curve gets 94% accuracy. From these results, it can be said that the use of cross-validation can increase the accuracy of this study.
Pengujian ISO 25010 Pada Smart Chair Akupresure Berbasis Internet Of Things (IoT) Diki Daryanto; M. Khairul Anam; Yoyon Efendi; Rahmaddeni Rahmaddeni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4134

Abstract

Smart chair acupressure is a technology that has been built using the application of the internet of things concept. The purpose of this study was to determine the quality level of the Acupressure Smart Chair, so that there are no errors in some features and the occurrence of errors in its functionality, which must be in accordance with the specified usability, the researchers conducted a test by utilizing the characteristics of ISO 25010. Related to the researcher's reasons taking ISO 25010 as one of the tests on the acupressure smart chair is because the smart chair acupressure technology cannot be said to be feasible because there has been no testing carried out in the measurement, the researchers used ISO 25010 which utilizes several characteristics that can be used such as functional suitability, usability and performance efficiency which focuses on measuring the electronic equipment used. Basically the concept used in this research is the internet of things which is a system that functions to control and simplify the workings of acupressure massage techniques for users. Whereas other problems that occur and arise often depend on customer satisfaction related to the quality provided on software and devices. This study uses the ISO 25010 method as the standard for the feasibility test on the device, where ISO 25010 basically also has 8 characteristics. In this study, to test the smart acupressure hair, the researchers used 7 characteristics that exist in ISO 25010, namely functional suitability, compatibility, usability, performance efficiency, reliability, maintability and portability. The instrument used is looking for an approach using the formula for reliability testing and processing on SPSS software. The results obtained from this study are that the acupressure smart chair has not been able to meet the ISO 25010 standard because the results obtained are 34.9% with an alpha cronbch value of 0.46 (unacceptable)
Sistem Marketplace Pencarian Lapangan Futsal Menggunakan Metode Haversine Berbasis Android Muhamad Jamaris; Hendra Saputra; M. Khairul Anam; Khusaeri Andesa; Rahmaddeni Rahmaddeni
Pixel :Jurnal Ilmiah Komputer Grafis Vol 15 No 1 (2022): Vol 15 No 1 (2022): Jurnal Ilmiah Komputer Grafis
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v15i1.712

Abstract

Futsal is a soccer game played indoors, this game itself is played by two teams, each team consisting of five people. At this time futsal is very much in demand from various groups ranging from children to adults, so there are many futsal field rental places, especially in the city of Pekanbaru. However, the problem that occurs is that it is still difficult for tenants to find out where there is a futsal field and the nearest futsal field from where the tenant is located, including price information and field facilities. Therefore, we need a system that can make it easier for tenants to find out where there is a futsal field and the nearest futsal field from the tenant's location. This system is made based on Android so that it can make it easier for users to use this system. The method used in making this system is the Haversine method. The results obtained are a marketplace system that can make it easier for users to find out where there is a futsal field, find the nearest field and it is also easy to find out all information related to the field and can make it easier to rent a futsal field through the marketplace system.
Chatbot Designing Information Service for New Student Registration Based on AIML and Machine Learning Yansyah Wijaya; Rahmaddeni; Fransiskus Zoromi
JAIA - Journal of Artificial Intelligence and Applications Vol. 1 No. 1 (2020): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (863.021 KB) | DOI: 10.33372/jaia.v1i1.638

Abstract

One of the efforts made by universities to serve prospective students is by providing consulting services and information that is usually carried out directly at the booth provided, through phone service or live chat support available on the college website. Increased visitors will result in waiting times due to limited availability of officers, which results in decreased satisfaction of prospective new students, moreover this service is only available during campus operating hours. One alternative solution to overcome this problem is to use Chatbot, able to answer questions raised by prospective new students which can be categorized as Frequently Asked Questions abbreviated as FAQ. Chatbot technology can be developed with a variety of AI (Artificial Intelligence) techniques. One of them is the AIML (Artificial Intelligence Markup Language) technique. One of the main drawbacks of AIML is that there is no reasoning ability so a learning system that is focused on supervised learning is needed. In the chatbot that will be built the learning process uses a selective neural conversational model or commonly called the Deep Semantic Similarity Model (DSSM) developed by Microsoft. Meanwhile, the measurement of chatbot performance will be done using Confusion Matrix which is a method of evaluating the performance of the algorithm from Machine Learning (ML). The results of the study stated that the chatbot system that was built was able to answer questions posed by prospective students properly and correctly while the questions were available in the chatbot knowledge base.
Data Mapping System Of Riau Province Fire Potential Using K-Means Clustering Method Rahmaddeni Deni; Andi Kurnianto
JAIA - Journal of Artificial Intelligence and Applications Vol. 1 No. 1 (2020): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (725.105 KB) | DOI: 10.33372/jaia.v1i1.640

Abstract

According to a report from the Riau Province BLHK states that hotspots in Riau Province are always present every year despite the number of hotspots that have been suppressed (http://dislhk.riau.go.id/). One of the causes is the frequent land clearing occurred as a trigger from a hotspot in Riau Province. There is a need for countermeasures as soon as possible to overcome the problem of hotspots that will cause forest fires. These problems need to be watched out quickly, one of which is to know in advance the hotspots that are likely to emerge based on existing data. Data mining processing is very suitable to be applied in order to produce relevant data to find out the possibility of hotspots. In this study the data grouping was done in the form of a visualization of hotspot mapping using the K-means Clustering method. The parameters used include 3 number of clusters (critical, alert, vigilant), 12 regencies / cities in Riau Province and 3 attributes (hotspots, number of fires, number of events). With the results of the visualization of the mapping using the K-means Clustering method, it is expected to be able to help the relevant parties, namely the Riau Provincial Forest Service in handling early the hotspots that are likely to emerge.
4 Star Complementary Food Menu Recommendation System Using the Mobile-Based Fuzzy Multiple Attribute Decision Making (FMADM) Method Rahmaddeni; Fransiskus Zoromi; Yansyah Saputra Wijaya; M. Khairul Anam
JAIA - Journal of Artificial Intelligence and Applications Vol. 1 No. 2 (2021): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1269.823 KB) | DOI: 10.33372/jaia.v1i2.793

Abstract

Toddler in the age category of six to twenty-four months should be ready to be given complementary food. In order to fulfill the nutritional needs for the toddler's growth, complementary foods must be sufficient for the kid according to their age while still paying attention to the continuity of breastfeeding. One thing that must be considered in choosing complementary foods is the Recommended Dietary Allowances (RDA) which is categorized by age, weight, and food texture, which is adjusted to the toddler age category. In terms of fulfilling all aspects of choosing complementary foods, this study proposes the design of a 4-star daily menu recommendation system for toddlers which refers to the intake of daily calorie needs for toddlers, namely carbohydrates, animal protein, vegetable protein, and vitamins/minerals using the FMADM method (Fuzzy Multiple Attribute Decision Making). The FMADM method used is the Electre method. In this study, the authors succeeded in building the desired recommendation system using the Electre method which produces a daily menu based on the number of mealtimes, based on the age and weight of toddlers by observing the user's tendency to the texture and composition of food and its nutritional content in the recommendation system that is built, so that can be accessed via mobile devices owned by the user.
Sentiment Analysis to analyze Vaccine Enthusiasm in Indonesia on Twitter Social Media M. Khairul Anam; Rahmaddeni; Muhammad Bambang Firdaus; Hadi Asnal; Hamdani
JAIA - Journal of Artificial Intelligence and Applications Vol. 1 No. 2 (2021): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (537.215 KB) | DOI: 10.33372/jaia.v1i2.794

Abstract

Vaccines are one way to prevent the coronavirus from entering the human body, although it is not 100% accurate. However, the implementation of vaccination in Indonesia is still controversial. People give their opinions directly or through social media such as Twitter. Retrieval of tweets using the Twitter API and using python. The data obtained is then preprocessed using case folding, cleaning, tokenizing, filtering, and stemming. After that, the model was evaluated using the Naive Bayes method. Naïve Bayes is a classification method that can predict the probability of a class to produce decisions based on learning data. Currently, nave Bayes is one of the methods to find accuracy in sentiment analysis that is often used and is the best. The results of this study obtained an accuracy of 79%.
Sentiment Analysis of Technology Utilization by Pekanbaru City Government Based on Community Interaction in Social Media Bunga Nanti Pikir; M. Khairul Anam; Hadi Asnal; Rahmaddeni; Triyani Arita Fitri; Hamdani
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 1 (2021): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (623.792 KB) | DOI: 10.33372/jaia.v2i1.795

Abstract

Government services for the public are currently utilizing technology, especially in the city of Pekanbaru. The government has currently centralized all services for the public, both online and offline, in public service malls. The type of service that uses technology, especially for online services, has received criticism in online media such as Twitter. To see the public's response to Pekanbaru city government services, especially in terms of technology, this study will use sentiment analysis to see positive, negative, and neutral comments. The method used is to see the accuracy generated using the Naïve Bayes Classifier (NBC) method. Bayes classifier is a statistical classifier, where the classifier can predict the probability of class membership of a data tuple that will fall into a certain class, according to the probability calculation. Accuracy results are obtained by dividing training data and testing data with a comparison of 70%:30% with an accuracy value of 55.56%, Precision 64%, recall 80%, f-score 71.2%.
Komparasi Algoritma Machine Learning Untuk Memprediksi Penyakit Alzheimer Firman Akbar; Rahmaddeni
Jurnal Komputer Terapan Vol. 8 No. 2 (2022): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (318.371 KB) | DOI: 10.35143/jkt.v8i2.5713

Abstract

Alzheimer's disease is a degenerative brain disease and the most common cause of dementia. It is characterized by deterioration of memory, language, problem-solving, and other cognitive skills that affect a person's ability to perform everyday activities. This decrease occurs because nerve cells (neurons) in parts of the brain involved in cognitive function are damaged and stop working properly. One way to detect Alzheimer’s is to use models of machine learning algorithms. In this study, the authors' team aimed to compare models of machine learning algorithms to find the one that gives better results in prediction Alzheimer's disease. Machine learning models algorithms in this study were built using Random Forest, Artificial Neural Network, Logistic Regression, Support Vector Machines, and Naive Bayes. The author's team then tested his 373 Alzheimer's disease patient data from Kaggle Open Datasets and showed that the Logistic Regression algorithm model can achieve better with 85,71% accuracy rate.
Co-Authors -, Dedek Ispandi A, M. Nakhlah Farid Adhitya Karel Maulaya Afiatuddin, Nurfadlan Agung Pratama Agung Pratama Agustin Agustin Agustin -, Agustin Agustin Agustin Agustin, Endy Wulan Agustriono Agustriono, Agustriono Aisy, Alaysha Rihadatul Aisyah Nurul Putri Akbar, Vitto Rezky Alaysha Rihadatul Aisy Aldino Evel Alfianda, Baginda Anam, M Khairul Ananta, Nita Anderson, Ranap Andi Kurnianto Andri Setiawan Anugraha, Yoga Safitra Aprilia, Fanesa Aprillian Kartino Arifin, Muhammad Amirul Asrofiq, Ahmad Aulia Putri Azdar, Qowiyyu Azfar Huzaifah Siregar B, Ilham Br.Situmorang, Elisabet Sinta Romaito Bunga Nanti Pikir Cahyo, M Rizky Dwi Chandra, Deni Cikita, Putri Cindy Syaficha Hardiana Dadynata, Eric Daulay, Suandi De Pani, Raihan Dedek Ispandi - Delfi Delfi, Delfi Deni Chandra Devi Efriadi Devi Puspita Sari, Devi Puspita Dhini Septhya Didik Sazali Diki Daryanto Dini, Ema Djamalilleil, Said Azka Fauzan Edwar Ali Efrizoni, Luasiana Efrizoni, Lusiana Eka, Wisnu Elma Novfuja Elwinda, Masyitah Erlin Ermy Pily, Annisa Khoirala Fadila, Rahmasari fadillah, m Fahreza, Rino Fakhrizal, M. Aggie Farhan Pratama Farida Try Puspa Siregar Fathoni, Muhammad Hafidhatul Fauzan, Aulia Fazri Fazri Febrio Waleska, Rangga Firman Akbar Firman, Muhammad Aditya fitri pratiwi, fitri Fransiskus Zoromi Fransiskus Zoromi Ginting, Alex Elanta Ginting, Lusiana Ginting, Steven Gusmansyah, Rafly Gusti Firmansyah, Mulia H A Supahri Habibah Br. Lumbantobing Hadi Asnal, Hadi Hafid Azis Supahri Hafidh, M. Hafidhatul Fathoni Hamdani Hamdani - Handoko Hanif Wira Saputra Hasnor Khotimah Hayami, Regiolina Hendra Saputra Hendrawan, Heri Herianto - Herianto Herianto Herisnan, Diva Nabila Huda, Isra Bil Ibrahim, Sang Adji Iftar Ramadhan Ihsan, Raja Muhammad Irawan , Sandra Septi Irawan, Sandra Septi Irsandi, Safril Jabbar, Fiqri Abdul Jamaris, Muhamad Jamaris, Muhammad Jasmarizal Jasmarizal Junadhi Junadhi Jundi, Muhamad Jundi, Muhammad Khairuddin, M. Kharisma Rahayu Khusaeri Andesa Koko Harianto Koko Harianto Koko Harianto, Koko Kurniawan, Bambang Kurniawan, Fadly Kurniawan, Zuprizal Lili Marlia Lusiana Efrizoni M Fadhil Arfa M. Arifin M. Azzuhri Dinata M. Irpan Mahdiawan Nurkholifah Mahendra, Muhammad Ihza Mardainis Mardainis Mardainis Marhadi, Nanda Maryani, Lily Maulana, Fitra Michal Dennis Muhaimin, Abdi Muhamad Rizky Dwi Cahyo Muhammad Adji Purnama Muhammad afrizal Muhammad Bambang Firdaus Muhammad Dzaki Salman Muhammad Fajri Jamil Muhammad Fikri Hidayat Muhammad Ridho Al Fathan Mukhsinin, Dimas Aditya Najario, Dimas nanda, afri Nanda, Annisa Nasution , Zikri Hardyan Nita Ananta Nova Indriyani Nurjayadi Nurjayadi Nurkholifah, Mahdiawan Oktavianda Perdana Arifin, Satria Pratama , Nanda Rizki Pratama, Farhan Pratiwi, Elsa Eka Prianto, Robi Purnama, Muhammad Adji Putra, Aldino Putra, Andika Mahesa Putra, Febrianda Putri Utami, Putri Putri, Adinda Dwi Putri, Daffina Zahro R Ismanizan Rabbani, Salsabila Rafliansyah, M Rahmat Hidayatullah Rahmi Rahmi Ramadhani, Jilang Ramadhansyah, Donny Rashid, Rashid Ratna Andini Husen Refni Wahyuni Renaldi, Reno Rinaldi Rinaldi Rini Yanti Rino Fahreza Risky Harahap Risman Risman Rivaldi, Ahmad Rizki Astuti Rizky Rahman Salam Rohana Yola Parastika Hutasoit Rohid Rohid, Rohid Rometdo Muzawi, Rometdo Ryan Ismanizan Safitri, Dea Sahelvi, Elza Salman, Muhammad Dzaki Salsabila Rabbani Sapina, Nur Sapitri, Riska Mela Saputra, Candra Saputra, Haris Tri Saputra, Ilham Saputra, Juliandri Saputra, Pingki Ans Satria, Riyan Sazali, Didik Septhya, Dhini Septia, Rapindra Setiawan , Andri Setiawan, Ahmad Agung Sholekhah, Fitriana Sigit, Rapel Aprilius Sinaga, Leonardo Singgih - Widiantoro Siregar, Azfar Huzaifah Soni Suhada, Khairus Sukri Adrianto Sukri Adrianto Supian, Acuan Susandri, Susandri SUSANTI Susanti Susanti Susanti, Susanti Sutisna Sutisna Syahrul Imardi Syarfi Aziz Syarifuddin Elmi T. Sy. Eiva Fatdha Tahiyat, Hafsah Fulaila Taupik Hidayat, Taupik Torkis Nasution Tri Revaldo, Bagus Triyani Arita Fitri Try Puspa Siregar, Farida Ulfa, Arvan Izzatul Ulfah, Aniq Noviciate Umar, Yusran Unang Rio Uthami, Kurnia Vindi Fitria wahyu, haditya Wahyudi, Gustri Romi Wicaksono, M Teguh Wicaksono, M. Teguh Widia Ningsih, Widia Wirta Agustin Wirta Agustin Wulandari, Denok Yansyah Saputra Wijaya Yesaya Twin Situmorang Yogi Yunefri, Yogi Yoyon Efendi Yuda Irawan Yulia Fatma Yusran Umar Yusril Ibrahim zairi saputra Zairi Saputra Zalianti, Fenisya Zega, Wilman Zikri Hadryan nst Zuriatul Khairi Zuriatul Khairi