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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

Author-ID : 2188003

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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Sentiment Analysis in Karonese Tweet using Machine Learning Ichwanul Muslim Karo Karo; Mohd Farhan Md Fudzee; Shahreen Kasim; Azizul Azhar Ramli
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 10, No 1: March 2022
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v10i1.3565

Recently, many social media users expressed their conditions, ideas, emotions using local languages on social media, for example via tweets or status. Due to the large number of texts, sentiment analysis is used to identify opinions, ideas, or thoughts from social media. Sentiment analysis research has also been widely applied to local languages. Karonese is one of the largest local languages in North Sumatera, Indonesia. Karo society actively use the language in expression on twitter. This study proposes two things: Karonese tweet dataset for classification and analysis of sentiment on Karonese. Several machine learning algorithms are implemented in this research, that is Logistic regression, Naive bayes, K-nearest neighbor, and Support Vector Machine (SVM). Karonese tweets is obtained from timeline twitter based on several keywords and hashtags. Transcribers from ethnic figures helped annotating the Karo tweets into three classes: positive, negative, and neutral. To get the best model, several scenarios were run based on various compositions of training data and test data. The SVM algorithm has highest accuracy, precision, recall, and F-1 scores than others. As the research is a preliminary research of sentiment analysis on Karonese language, there are many feature works to improvement.

Comparative Analysis of K-Nearest Neighbor and Modified K-Nearest Neighbor Algorithm for Financial Well-Being Data Classification Ichwanul Muslim Karo Karo
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 3 (2021): December, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.3.593

Financial Well-Being is the condition that a person has been able to meet current and future financial obligations. There are many parameters in determining people who have obtained financial well-being. Classification is a data mining task that can be used to identify someone with financial well-being. One of the most popular classification algorithms is K Nearest Neighbor (KNN). However, there is also a Modified K Nearest Neighbor (MKNN) classification algorithm which is an extended KNN. In this paper, we will analyze a comparison of KNN and MKNN algorithms to classify financial well-being datasets. Comparative analysis is based on the accuracy and running time of both algorithms. Prior to the classification process, K-Fold Cross Validation was performed to find the optimal data modeling. The results of the K Fold Cross Validation modeling will be a model for the sample of training data and data testing. Evaluation of classification results based on precision, recall, and F-1. The test resulted in a higher KKN performance compared to MKNN in all test parameters, with an average gap of 25 percent. In addition, it was also found that the execution time of the KNN algorithm was faster than that of the MKNN

PENINGKATAN DIGITAL MARKETING DAN PENGUATAN MEREK DI MEDIA DIGITAL PADA KOMUNITAS SENTRA KREASI Rennyta Yusiana; Bachruddin Saleh Luturlean; Rohmat Saragih; Retno Setyorini; Wardhani Muhamad; Ichwanul Muslim Karo Karo; Heru Nugroho; Yahya Peranginangin
Charity : Jurnal Pengabdian Masyarakat Vol 5 No 1 (2022): Charity-Jurnal Pengabdian Masyarakat
Publisher : PPM Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/charity.v5i1.3916

Saat ini sosial media dapat digunakan sebagai media untuk melakukan transaksi produk bisnis yang dimiliki. Media yang biasa digunakan untuk digital marketing seperti: website, sosial media, email marketing, video marketing, iklan, SEO, dan lainnya. Digital marketing bertujuan untuk menjangkau pasar lebih luas dengan media internet. Setelah terhubung dengan calon konsumen, kita dapat melakukan edukasi kepada calon konsumen sekaligus melakukan branding terkait produk atau jasa yang kita jual. Di Kabupaten Bandung terdapat suatu Komunitas UMKM yaitu Komunitas Sentra Kreasi yang beranggotakan kurang lebih dari 150 anggota aktif. Produk yang dihasilkan beraneka ragam seperti: pakaian, makanan dan kerajinan. Saat ini pemasaran dengan sosial media belum optimal, karena sulitnya anggota UMKM memahami teknologi. Hal ini menjadi hambatan Komunitas Sentra Kreasi untuk merambah pasar yang lebih luas. Karena itu diadakan workshop tentang pemasaran dengan sosial media, yang bertujuan mengoptimalisasi dan meningkatkan penjualan. Luaran lain yaitu Pembangunan dan tutorial Web Media Edukasi Digital Marketing untuk anggota komunitas agar mudah menggunakan media online sebagai sarana pemasaran, diharapkan anggota Sentra Kreasi dapat memasarkan produknya dan mampu mengelola penjualan produk secara terpadu. Kegiatan Pengabdian kepada Masyarakat ini dilaksanakan oleh dosen dan mahasiswa Fakultas Ilmu Terapan (FIT) dan Fakultas Komunikasi Bisnis (FKB) Universitas Telkom.

Pengaruh Metode Pengukuran Jarak pada Algoritma k-NN untuk Klasifikasi Kebakaran Hutan dan Lahan Ichwanul Muslim Karo Karo; Ananda Khosuri; Juan Steiven Imanuel Septory; Dimas Pebrian Supandi
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.v6i2.3967

Forest and land fires are a serious and recurring problem in Indonesia. The high intensity of forest fires is caused by the distribution of hotspots in fire-prone areas. One of the efforts to prevent and minimize the risk of forest fires is to identify the types of hotspots using a classification approach. One of the most popular classification algorithms is k Nearest Neighbor (k-NN). The algorithm uses a distance calculation approach in classifying objects. The purpose of this study is to classify the types of hotspots scattered in Indonesia using the k-NN algorithm and to analyze the effect of the distance calculation method on the k-NN algorithm. The types of distance measurement methods analyzed include Euclidean, Canberra, Chebyshev, and Manhattan. The dataset used is the distribution of hotspots in Indonesia obtained from Global Forest Watch (GFW). The study designed a dataset with two conditions, through the pre-processing stage and not. In general, the model accuracy of the k-NN combination with various distance measurement methods is above 90%. The pre-processing stage can increase the model's performance 1-8 times. The combination of k-NN with Manhattan is the best choice to identify the types of hotspots with an accuracy of 92.6%.

Analysis of Expertise Group Using The Fuzzy K-NN Classification Algorithm (Case Study: School of Computing Telkom University) Jodi Kusuma; Angelina Prima Kurniati; Ichwanul Muslim Karo Karo
JURIKOM (Jurnal Riset Komputer) Vol 9, No 3 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i3.4215

The School of Computing at Telkom University has four Expertise Groups that defines the lectures taken by students. Deciding the Expertise Group, will be influential in deciding elective courses and raising the topic of the Final Project. There are many students who are still having difficulty in deciding the Expertise Group and finally only decide based on the most popular Expertise Group without seeing their potential and abilities. The impact of wrong decision of the Expertise Group are delays in graduation time. It will then affect accreditation of study program and university rank, especially in the timely graduation indicator. Therefore, it is necessary to have a system that can predict the decision of the Expertise Group for the School of Computing students based on their academic scores. In this study, prediction using the Fuzzy K-Nearest Neighbor classification algorithm was chosen because it can determine the class based on the nearest neighbor and consider ambiguous data because of the weighting value in each class. There are five tests carried out to get the best model, namely (1) examine the best split training and validation data, (2) examine the best K value, (3) compare Fuzzy K-Nearest Neighbor with Naïve Bayes and Decision Tree (C4.5) which is a commonly used classification algorithm, (4) examine the values of accuracy, precision, recall, f1-score, and (5) examine the values of accuracy using Cross-Validation method. The result is that the model made using Fuzzy K-Nearest Neighbor has an accuracy value of 72% in the case of imbalance data, 62% in the case of applying the undersampling technique, and 56% in the case of applying oversampling. Based on experiments with the other two algorithms, it was found that compared to the other two algorithms, the Fuzzy K-Nearest Neighbor has a higher accuracy value in the case of imbalance data and the case of applying to undersampling, but it has a lower accuracy in the case of applying oversampling, due to the lack of Fuzzy K-Nearest Neighbor in handling small minority data variations.

Wildfires Classification Using Feature Selection with K-NN, Naïve Bayes, and ID3 Algorithms Ichwanul Muslim Karo Karo; Sisti Nadia Amalia; Dian Septiana
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 3, No 1: June 2022
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/seict.v3i1.47537

Wildfires are a problem with a high intensity of occurrence and recurrence in Indonesia. If this problem is not properly addressed, it will threaten air circulation in the world. The source of fire can be natural or man-made. As a preventive measure for the widespread spread of fire, it is necessary to investigate the type of fire early on so that it can be determined the type of fire with the highest priority to be extinguished immediately. The process of identifying fire types can be done by classification. This research aims to classify the type of fire with three algorithms, namely K-Nearest Neighbour (K-NN), Naïve Bayes and Iterative Dichotomise 3 (ID3). The forest fire dataset was obtained from the Global Forest Watch (GFW) platform. Before entering the classification stage, the dataset went through a feature selection process, where attributes meeting the threshold were selected for the classification process. The performance of ID3 algorithm is superior compared to other algorithms with an accuracy of 65.83, precision 67.4, recall 67.02 and F1 67.21 per cent. Finally, the feature selection process contributes positively to the classification process, increasing the model performance by 2-5 per cent.

Segmentation of Credit Card Customers Based on Their Credit Card Usage Behavior using The K-Means Algorithm Ichwanul Muslim Karo Karo
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 2, No 2: December 2021
Publisher : Universitas Pendidikan Indonesia (UPI)

The intensity of credit card customers in making transactions has increased in the last 10 years in Indonesia. This is both a challenge and an opportunity for the Bank. Customer segmentation information is beneficial to reduce bad debts or increasing customer credit card limit capacity. This study aims to segment credit card customers based on their usage behavior with a clustering approach using the K-means algorithm. While the process of evaluating segmentation results using the silhouette index. Based on the experimental results, six groups are the best number of clusters. The six groups are shopping hobbies, payment process at maturity, payment by installments, withdrawing cash, buying expensive goods, and types that rarely use credit cards.

Prediksi Penyebaran Demam Berdarah Dangue dengan Algoritma Hybrid Autoregressive Integrated Moving Average dan Artificial Neural Network: Studi Kasus di Kabupaten Bandung Ichwanul Muslim Karo Karo
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 2, No 1: June 2021
Publisher : Universitas Pendidikan Indonesia (UPI)

Demam Berdarah Dengue (DBD) merupakan penyakit menular yang ditularkan melalui gigitan nyamuk Aedes Aegypti. WHO (World Health Organization) telah mengupayakan langkah-langkah pencegahan terhadap wabah DBD dengan penerapan teknologi. Teknologi yang digunakan untuk mencegah penyebaran wabah DBD adalah penggunaan serangkaian proses komputasi untuk menghasilkan prediksi penyebaran DBD yang diharapkan dapat membantu langkah pencegahan. Dalam membantu pengembangan teknologi pencegahan DBD penulis mengembangkan model hybrid Autoregressive Integrated Moving Average (ARIMA) dan Artificial Neural Network (ANN) untuk membantu memprediksi incident rate DBD berdasarkan beberapa variabel terkait seperti cuaca dan incident rate yang diambil dari Januari 2009 – November 2016. Dari model hybrid ARIMA dan ANN dihasilkan nilai prediksi yang memiliki tingkat error yang rendah yang diindikasikan oleh nilai RMSE yang kecil. Model hybrid ARIMA-ANN yang optimal adalah hybrid ARIMA-ANN dengan orde (1,0,3) dengan nilai RMSE sebesar 0.0087

Implementasi Metode XGBoost dan Feature Important untuk Klasifikasi pada Kebakaran Hutan dan Lahan Ichwanul Muslim Karo Karo
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 1, No 1: December 2020
Publisher : Universitas Pendidikan Indonesia (UPI)

Kebakaran hutan dan lahan di Indonesia telah menjadi masalah krisis lingkungan tahunan. Sebaran kebakaran hutan terbesar terjadi dipulau Sumatera. Salah satu upaya tindakan dalam pencegahan dan meminimalisasikan resiko kebakaran hutan adalah dengan mengklasifikasikan jenis titik panas di lahan, sehingga di dapat skala prioritas dalam pemadaman titik api. Penelitian ini bertujuan mengklasifikasikan type titik panas dengan metode XGBoost dan feature importance yang terdapat di pulau Sumatera. Data titik panas diperoleh dari Globalforestwatch.com. Proses mengurangi variabel dari data yang diperoleh menghasil dampak yang sangat signifikan pada model klasifikasi. Terapat enam dan atau tujuh variabel yang sangat berpengaruh dalam menentukan titik panas, variabel tersebut jugalah yang menghasilkan model klasfikasi terbaik. XGBoost dan feature importance menghasilkan akurasi sebesar 89.52%. Sensitivity (SE), Specificity (SP), dan Matthews Correlation Coefficient (MCC).secara berturut turut 91.32 %, 93.16 % dan 92.75 %. Metode ini juga lebih baik dibandingkan dengan hasil penelitian sebelumnya.

Movie recommender chatbot based on Dialogflow Zinke Abdurahman Baizal; Nurul Ikhsan; Ichwanul Muslim Karo Karo; Reinaldo Kenneth Darmawan; Roby Dwi Hartanto
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp936-947

Currently, the online movie streaming business is growing rapidly, such as Netflix, Disney+, Amazon Prime Video, HBO, and Apple TV. The recommender system helps customers in getting information about movies that are in accordance with their wishes. Meanwhile, the development of messaging platform technology has made it easier for many people to communicate instantly. Utilizing a messaging platform to build a recommender system for movies, provides special benefits because people often access the messaging platform all the time. In the Indonesian language, there are many slang terms that the system must recognize. In this study, we build a chatbot on a messaging platform which users can interact with the system in natural language (in Indonesian language) and get recommendations. We use rule-based and maximum likelihood as a method in natural language processing (NLP), and content-based filtering for the recommendation process. The recommender system interaction is built through a conversation mechanism that will form a conversational recommender system. The interaction is based on a chatbot which is built using Dialogflow and implemented on the telegram. We use the accuracy of recommendations and user satisfaction to evaluate the system performance. The results obtained from the user study indicate that the NLP approach provides a positive experience for users. In addition, the system also produces an accuracy value of 83%.

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

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