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All Journal International Journal of Electrical and Computer Engineering Jupiter Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Indonesian Journal on Computing (Indo-JC) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA BAREKENG: Jurnal Ilmu Matematika dan Terapan JURIKOM (Jurnal Riset Komputer) Jurnal Teknologi Terpadu STRING (Satuan Tulisan Riset dan Inovasi Teknologi) MULTINETICS Jurnal Mantik JATI (Jurnal Mahasiswa Teknik Informatika) Sains, Aplikasi, Komputasi dan Teknologi Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Charity : Jurnal Pengabdian Masyarakat Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Journal of Software Engineering, Information and Communication Technology Jurnal Teknik Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Asian Journal of Community Services (AJCS) eProceedings of Engineering Tekmulogi: Jurnal Pengabdian Masyarakat Journal of Law, Education and Business Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Journal of Mathematics, Computation and Statistics (JMATHCOS)
Ichwanul Muslim Karo Karo
Universitas Negeri Medan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Karonese Sentiment Analysis: A New Dataset and Preliminary Result Ichwanul Muslim Karo Karo; Mohd Farhan Md Fudzee; Shahreen Kasim; Azizul Azhar Ramli
JOIV : International Journal on Informatics Visualization Vol 6, No 2-2 (2022): A New Frontier in Informatics
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2-2.1119

Abstract

Amount social media active users are always increasing and come from various backgrounds. An active user habit in social media is to use their local or national language to express their thoughts, social conditions, socialize, ideas, perspectives, and publish their opinions. Karonese is a non-English language prevalent mostly in North Sumatra, Indonesia, with unique morphology and phonology. Sentiment analysis has been frequently used in the study of local or national languages to obtain an overview of the broader public opinion behind a particular topic. Good quality Karonese resources are needed to provide good Karonese sentiment analysis (KSA). Limitation resources become an obstacle in KSA research. This work provides Karonese Dataset from multi-domain social media. To complete the dataset for sentiment analysis, sentiment label annotated by Karonese transcribers, three kinds of experiments were applied: KSA using machine learning, KSA using machine learning with two variants of feature extraction methods. Machine learning algorithms include Logistic Regression, Naïve Bayes, Support Vector Machine and K-Nearest Neighbor. Feature extraction improves model performance in the range of 0.1 – 7.4 percent. Overall, TF-IDF as feature extraction on machine learning has a better contribution than BoW. The combination of the SVM algorithm with TF-IDF is the combination with the highest performance. The value of accuracy is 58.1 percent, precision is 58.5 percent, recall is 57.2, and F1 score is 57.84 percent
Klasifikasi Penderita Diabetes menggunakan Algoritma Machine Learning dan Z-Score Ichwanul Muslim Karo Karo; Hendriyana Hendriyana
Jurnal Teknologi Terpadu Vol. 8 No. 2 (2022): December, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.564

Abstract

Diabetes is a deadly and chronic disease. It characterized by an increase in blood sugar. Many complications occur if diabetes does not treat and identified. The common identification process by visits to diagnostic centers and consulting physician. It makes bored patients. Machine learning approach can solve the problem of diabetic identification. However, the unbalanced range of diabetes variable values ​​affects the quality of machine learning results. This study predicts the likelihood of diabetes in diabetic patients from 768 Indian women, using three machine learning classification algorithms and Z-Score normalization method. The machine learning algorithms used are Decision Tree, Support Vector Machine (SVM) and Naive Bayes. Experiments were run on the Pima Indians Diabetes Database (PIDD). Dataset retrieved from the UCI Machine Learning Repository. The performance of the three algorithms was evaluated using accuracy, precision, F1, and recall based on confusion matrix. SVM algorithm is an algorithm that has the highest performance that both algorithm the Naive Bayes and Decision Tre algorithms, the accuracy and F1 is 80.73% and 76%. The Z-Score method has positively contribution to increasing the accuracy of the classification model. Furthermore, this study also managed to get a higher accuracy than previous studies.
Analisis perbandingan Algoritma Support Vector Machine, Naive Bayes dan Regresi Logistik untuk Memprediksi Donor Darah Hendriyana Hendriyana; Ichwanul Muslim Karo Karo; Sri Dewi
Jurnal Teknologi Terpadu Vol. 8 No. 2 (2022): December, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v8i2.581

Abstract

Blood supplies and stocks are urgently needed. Regular donations from healthy volunteers are the only way to keep up with the blood supply. This research aims to develop and evaluate a machine-learning algorithm to predict whether a volunteer will donate or not. The machine learning algorithms are Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). This study also applies the process of normalizing data with a Z-score to standardize the dataset scale. The dataset is sourced from the Hsin-Chu City Blood Transfusion Service, Taiwan, and stored in the UCI repository. The evaluation methods are accuracy, precision, recall, and F-1 score. The research results with the Naïve Bayes algorithm were 89.90%, Logistic Regression 82.59%, and SVM 94.79%. The normalization process using the Z-Score method contributes positively to improving the performance of the classification model. Based on this performance, it provides predictive results for volunteers who will return to donate blood to offer blood reserves to those in need.
Design of a geographic information system (gis) for the spread of covid-19 disease in medan city Melania Justice Panggabean; Yulita Molliq Rangkuti; Ichwanul Muslim Karo-Karo
Jurnal Mantik Vol. 6 No. 4 (2023): February: 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.v6i4.3437

Abstract

The world is currently facing an outbreak of the corona virus (Covid-19). Covid 19 is a group of highly diverse, enveloped, single-stranded RNA viruses. This disease causes respiratory tract infections in humans with severity ranging from mild to fatal. Examples of mild illnesses such as influenza while for deadly diseases such as MERS and SARS. Medan city is one of the areas that is prone to Covid-19 disease. Where the spread of Covid-19 has reached 16.4% the positive case rate with an assessment of the Covid-19 situation as of February 2022 is at Level 4. So, we need a sytem that can monitor the progress of the case. The purpose of this research is to build a geographic information sytem using the Fuzzy C-Means method and integrate GeoJSON to map the spread of the Covid-19 disease in Medan City.  The results of clustering calculations using the Fuzzy C-Means method yield the following results: Cluster 1 which contains the sub-districts of Medan Amplas, Medan Area, Medan Baru, Medan Barat, and Medan Perjuangan is in the green zone. Cluster 2 which contains the sub-districts of Medan Denai, Medan Tembung, Medan Petisah, Medan Kota, and Medan Timur is in the red zone. Cluster 3 which contains the sub-districts of Medan Tuntungan, Medan Selayang, Medan Johor, Medan Sunggal, and Medan Helvetia is in the orange zone. And Cluster 4 which contains the sub-districts of Medan Polonia, Medan Maimun, Medan Deli, Medan Labuhan, Medan Marelan, and Medan Belawan is in the yellow zone. 
Simulasi Proses Desalinasi Air Laut Menggunakan Energi Listrik Menjadi Air Ichwanul Muslim Karo Karo; Sri Suryani
eProceedings of Engineering Vol 2, No 1 (2015): April, 2015
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Desalinasi adalah suatu proses pengurangan kadar garam untuk memperoleh air mineral dari air laut. Dalam tugas akhir ini dipresentasikan model matematika dari proses desalinasi air laut dengan memanfaatkan energi listrik, memperhatikan aspek suhu, luas penampang, dan volume awal air laut. Model matematika yang dibangun meliputi laju peningkatan suhu dengan pendekatan persamaan kalor jenis, laju penguapan dengan pendekatan Irving langmuir, volume optimal air mineral yang diperoleh, laju kadar garam dan laju peningkatan volume dengan pendekatan volume gas ideal. Model matematika diverifikasi dengan prinsip hukum kekekalan volume dan dibandingkan dengan hasil implementasi desalinasi air laut di perusahaan dan selanjutnya dibangun simulasi desalinasi air laut. Dua liter air laut dengan energi listrik 150 kJ dapat dioptimalkan hingga 1,86 liter air mineral dengan waktu didih 124,014 detik, dan lebih baik daripada implementasi perusahaan yang hanya mampu mengoptimalkan 5 per tujuh bagian dari air laut. Kata kunci :desalinasi, model, simulasi, metode irving langmuir
Hoax Detection on Indonesian Tweets using Naïve Bayes Classifier with TF-IDF Ichwanul Muslim Karo Karo; Romia Romia; Sri Dewi; Putri Maulidina Fadilah
Journal of Information System Research (JOSH) Vol 4 No 3 (2023): April 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i3.3317

Abstract

Twitter is one of the most popular social media platforms in the world nowadays. Twitter users in Indonesia are the fifth largest in the world and are always active in expressing themselves and getting information through tweets. A hoax is a lie created as if it were true. Hoaxes are also often spread via tweets. The spread of hoaxes is extremely dangerous because it can cause social discord and even misunderstanding. Therefore, hoaxes must be resisted. This study aims to build a system to detect hoaxes on Indonesian tweets. The objective of this research is to identify hoax Indonesian tweets by using the Naïve Bayes classifier with Term Frequency Inverse Document Frequency (TF-IDF). This study collects and annotates tweets from hoax tweets post which sent by a user account. This study also applied several text preprocessing techniques to provide datasets. To provide the best hoax prediction model, this work splits datasets into training and testing datasets. There are four experimental scenarios that refer to splitting the dataset. The experimental results showed that the hoax prediction model using Naïve Bayes with TF-IDF had 64% accuracy and recall, 69% and 67% precision, and a F1-score respectively. This result is also superior to the hoax prediction model when using the Naïve Bayes classifier without the TF-IDF. It means that TF-IDF has made a positive contribution to improving model performance. Finally, this research contributes by detecting news with a proclivity for hoaxes and filtering what is classified as hoaxes or not.
Pelatihan media e-learning classroom untuk guru SMKN 1 Peureulak Timur Ichwanul Muslim Karo Karo; Widi Astuti; Ramanti Dharayani
TEKMULOGI: Jurnal Pengabdian Masyarakat Vol 2, No 2 (2022): November 2022
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3755.966 KB) | DOI: 10.17509/tmg.v2i2.48963

Abstract

One of the sectors affected by the covid-19 pandemic is the education sector. SMKN 1 Peureulak Timur as the only vocational high school in Peureulak Timur sub-district, East Aceh must continue to provide education services for all students of the best quality. One of the efforts to improve educating and teaching skills for teachers at SMKN 1 Peureulak Timur is by providing e-learning training. One of the e-learning media that is often used is Goggle Classroom because of its complete features and quite easy to operate. Therefore, the activity that will be carried out at this community dedication is Google Classroom training. It is hoped that after receiving this Google Classroom training, teachers of SMKN 1 Peureulak Timur can improve their performance in facing the Industrial Revolution 4.0.
Determination of Mango Fruit Maturity on the Tree Based on Digital Image Processing and Artificial Neural Networks Aditia Sanjaya; Ichwanul Muslim Karo Karo
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 4, No 1: June 2023
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/seict.v4i1.52916

Abstract

Until now, humans have determined the ripeness of mangoes on the tree by hand. Losses are caused by the insecurity of the human state and a misunderstanding of the maturity level of mangoes. In the future, a system that can detect the ripeness of mangoes on the tree will be required. This research provides a preliminary examination of the technology's implementation. The study created a computerized image processing method for determining the ripeness of mangoes on the tree. The neural network backpropagation algorithm was employed in this investigation. The feature extraction model employed in the image is a hybrid of the RBG and HSV models. The best accuracy level is 72%, with an 80:20 ratio of test data to training data. 
Analisis Sentimen Ulasan Aplikasi Info BMKG di Google Play Menggunakan TF-IDF dan Support Vector Machine Ichwanul Muslim Karo Karo; Justaman Arifin Karo Karo; Yunianto Yunianto; Hariyanto Hariyanto; Miftahul Falah; Manan Ginting
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3943

Abstract

Posting online reviews has become one of the most popular ways to express opinions and sentiments towards service applications. The Meteorology, Climatology and Geophysics Agency (BMKG) Info application is an Android and iOS-based mobile application that provides information on weather, climate, air quality, and earthquakes that occur in various regions in Indonesia. The information contained in this application is very important but has a worse value than other forecasting applications. Sentiment analysis is the process of classifying text into several classes such as positive sentiment, negative or not containing both. This research aims to analyze user reviews on the BMKG Info application from the Google Play website. The benefits obtained are as consideration for developers to improve the shortcomings of the application. The classification process uses Term Frequency-Inverse Document Frequency (TF-IDF) and the Support Vector Machine (SVM) algorithm. This research successfully collected 2500 reviews from users of the BMKG Info application on the Google PlayStore website using the web scraping method. Text preprocessing of the reviews used case folding, symbolic and stopword removal, tokenization, normalization, and stemming. User ratings help in identifying the sentiment label of a review, 66% of reviews are positive while the rest are negative. The most frequently reviewed topics with sentiment value are "application", "information", "update". This research conducted three experimental scenarios based on the composition of training data and test data. Based on the prediction model, the scenario with 75%:25% split data has the highest accuracy rate of 79%.
Process Mining pada Proses Penerimaan Mahasiswa Baru di Telkom University dengan Genetic Miner Supra Yogi; Angelina Prima Kurniati; Ichwanul Muslim Karo Karo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.5241

Abstract

The selection process for new students at Telkom University, also known as SMB Telkom University has been running for years and already has its process flow. However, the existing process flow can be further improved to better reflect the actual field processes and become more accurate. Process mining can enhance this process flow by creating a new process flow based on event logs or previously executed processes. One of the algorithms in process mining is genetic process mining, where process mining is performed multiple times over several generations and genetic algorithms such as crossover and mutation are applied to generate a more accurate process model compared to other process mining algorithms such as heuristic and inductive mining. After conducting experiments, the best process model that was produced was at the 100th generation which has a fitness point of 0.755910819 and precision point of 0.742857143, after examining the parameters and the resulting Petri net or process flow that was produced it was concluded that the process model obtained from the application of Genetic Process Mining to SMB Telkom University is not very good because the resulting Petri net has several duplicate activities and appears to be non-linear. This could be due to several factors i.e., incompatible, or inaccurate data.
Co-Authors Abil Mansyur, Abil Adawiah Hasyani, Rabiahtul Ade Amelia, Tasya Adidtya Perdana, Adidtya Aditia Sanjaya Ahyar, Khoirul Ananda Khosuri Angelina Prima Kurniati Anggraini, Nisa Putri Aqila Aqila, Aqila Azizul Azhar Ramli Azizul Azhar Ramli Bachruddin Saleh Luturlean Bakti Dwi Waluyo Darari, Muhammad Badzlan Daulay, Leni Karmila Dedy Kiswanto Dian Septiana Dimas Pebrian Supandi Esra Kristiani Sihite Ester Berliana Ritonga, Yolanda Eviyona Laurenta Br Barus Fadillah, Wahyu Nur Falah, Miftahul Fitri Rahayu Fitria, Nur Anisa Gea, Kurnia Mildawati Ginting, Manan Gunawan, Rizky Habibi, Rizki Haraha, Melyana Hariyanto HARIYANTO HARIYANTO Hariyanto Hariyanto Hariyanto, Hariyanto Hendriyana Hendriyana Heru Nugroho Husna Batubara, Shabrina Ida Ayu Putu Sri Widnyani Jodi Kusuma Juan Steiven Imanuel Septory Justaman Arifin Karo Karo Karo karo, Justaman Arifin Karo Karo, Justaman Arifin Landong, Ahmad Lorinez S, Yohana Manan Ginting Mardiana Mardiana Maretha Br. Simbolon, Silvana Maulana Malik Fajri Maulidna, Maulidna Melania Justice Panggabean Miftahul Falah Miftahul Falah Mohd Farhan Md Fudzee Mohd Farhan Md Fudzee Molliq Rangkuti, Yulita Mufida, Yasmin Muhammad Yusuf Mutiara Sihaloho, Laura Adelia Nasution, Aurela Khoiri Natasya, Amanda Nelza, Novia Nur Hafni Nurul Ain Farhana Nurul Ikhsan Panggabean, Suvriadi Permata Putri Pasaribu, Yohanna Purba, Desni Paramitha Putri Harliana Putri Maulidina Fadilah Ramadhani, Fanny Ramanti Dharayani Rangkuti, Y. M Reinaldo Kenneth Darmawan Rennyta Yusiana Retno Setyorini Roby Dwi Hartanto Rohmat Saragih Romia Romia Said . Iskandar Salsabila, Aqila Shahreen Kasim Shahreen Kasim Simamora, Elmanani Sisti Nadia Amalia Sri Dewi Sri Dewi Sri Dewi Sri Dewi Sri Suryani Supra Yogi Syahrin , Alvin Valentino, Bob Wahyu Nur Fadillah Wardhani Muhamad Warjaya, Angga Wibowo, Adinda Widi Astuti Winsyahputra Ritonga Yahya Peranginangin Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yulita Molliq Rangkuti Yunianto Yunianto Yunianto Yunianto Yunianto Yunianto, Yunianto ZK Abdurahman Baizal