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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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Implementasi Algoritma Support Vector Machine Untuk Analisis Sentimen Aplikasi Easycash di Playstore Maulana Malik Fajri; Ichwanul Muslim Karo Karo
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 1 No. 3 (2023): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.572349/scientica.v1i3.435

Abstract

Analisis sentimen ialah sebuah metode yang dipergunakan untuk memperoleh data dari opini, memahami serta mengolah tekstual data secara otomatis untuk melihat sentimen yang terkandung dalam sebuah opini. Support Vector Machine (SVM) ialah salah satu algoritma klasifikasi yang dapat digunakan untuk sentimen analisis. Penelitian ini bertujuan mengklasifikasikan review aplikasi Easycash di Google Play Store menggunakan analisis sentimen yang telah dikumpulkan dan disortir. Hasil dari penelitian ini sangat berguna bagi pemilik aplikasi untuk mengambil keputusan di masa depan. Penelitian ini menggunakan 2500 data ulasan aplikasi Easycash dari Google Play Store. Pada tahap pertama penelitian ini menggunakan Case Folding, Filtering, Tokenizing, Slang Word, Stopwords, Stemming, kemudian melakukan Konversi kalimat lalu mentransformasi teks ke vector dengan TfidfVectorizer. Pada tahap kedua melakukan spilt data menjadi dua bagian dengan perbandingan 20% dan 80%, yaitu 80% bagian untuk data training dan 20% bagian untuk data testing. Pada tahap terakhir membangun model sehingga diperoleh akurasi sebesar 89%, precision negatif sebesar 82% dan positif sebesar 94%, serta recall negatif sebesar 92% serta positif sebesar 87% dan f1-score negative sebesar 87% juga f1-score positive sebesar 90%.
K-Means and K-Medoids Algorithm Comparison for Clustering Forest Fire Location in Indonesia Ichwanul Muslim Karo Karo; Sri Dewi; Mardiana Mardiana; Fanny Ramadhani; Putri Harliana
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 10 No 1 (2023): Jurnal Ecotipe, April 2023
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v10i1.3896

Abstract

Forest fires are the most common cause of deforestation in Indonesia. This condition has a negative impact on the survival of living things. Of course, this has received special attention from various parties. One effort that can be made for prevention is to group these points into areas with the potential for fire using the clustering method. In this research, a comparative study of the clustering algorithm between K-Means and K-Medoids was conducted on hotspot location data obtained from Global Forest Watch (GFW). Besides that, important variables that affect the clustering process are also analyzed in terms of feature importance. There are nine important variables used in the clustering process, of which the Acq_time variable is the most important. The cluster quality of both algorithms is evaluated using the silhouette coefficient (SC). Both algorithms are capable of producing strong clusters. The best number of clusters is six clusters. The K-medoids algorithm is better at grouping data than K-means.
Pembangunan Webgis Untuk Penderita Gizi Buruk Di Kota Medan Berdasarkan Hasil Clustering Algoritma DBSCAN Esra Kristiani Sihite; Yulita Molliq Rangkuti; Ichwanul Karo Karo
Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Vol 23 No 1 (2024): Februari 2024
Publisher : PRPM STMIK TRIGUNA DHARMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jis.v23i1.9528

Abstract

Gizi buruk merupakan kondisi serius di mana berat badan balita jauh lebih rendah dibandingkan tinggi badannya akibat asupan nutrisi yang kurang memadai untuk pertumbuhannya. Gizi buruk dapat memiliki konsekuensi jangka panjang pada perkembangan anak, serta meningkatkan risiko morbiditas dan mortalitas. Tujuan dari penelitian ini adalah untuk mengembangkan Sistem Geografis (SIG) yang menggunakan metode Density Based Spasial Clustering of Application with Noise (DBSCAN) untuk memetakan kasus gizi buruk di Kota Medan. Metode DBSCAN digunakan untuk mengelompokkan data kasus gizi buruk berdasarkan karakteristik yang serupa dan untuk memancarkan hasil pemetaan dengan menggunakan Silhouette Index dan Index Dunn. Selain itu, peneliti juga membangun Sistem Informasi Geografis untuk visualisasi penyebaran gizi buruk, dan menguji sistem dengan Blackbox Testing. Berdasarkan perbandingan validasi cluster, hasil Silhouette Index sebanyak 0,5414 sedangkan Index Dunn sebanyak 0,5124. Selain itu, berhasil mengembangkan Sistem Informasi Geografis (SIG) untuk memetakan kasus gizi buruk di Kota Medan. Sistem ini dirancang khusus untuk Dinas Kesehatan Kota Medan dengan tujuan memberikan informasi yang lebih efisien dalam pemetaan, pemantauan dan pengambilan keputusan terkait penanganan gizi buruk.
Implementasi Text Summarization Pada Review Aplikasi Digital Library System Menggunakan Metode Maximum Marginal Relevance Ichwanul Muslim Karo Karo; Sri Dewi; Adidtya Perdana
JEKIN - Jurnal Teknik Informatika Vol. 4 No. 1 (2024)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v4i1.671

Abstract

Peringkasan teks merujuk pada pembuatan rangkuman teks secara otomatis dengan pendekatan natural language processing (NLP).  Text summarization dibutuhkan saat jumlah dokumen atau review yang akan dirangkum dalam jumlah yang banyak. Sebuah rangkuman yang dihasilkan dapat menjadi pengetahuan, masukan maupun saran untuk perbaikan/pengembangan berbagai aplikasi. Aplikasi Digital Library System merupakan sebuah mobile apps untuk layanan perpustakaan Universitas Negeri Medan (Unimed). Aplikasi tersebut memiliki banyak ulasan di berbagai platform. Tentu, rangkuman ulasan tersebut merupakan pengalaman pengguna dan dapat menjadi masukan untuk pengembangan versi terbaru. Namun menjadi tantangan jika seluruh ulasan pengguna dirangkum secara manual, karena akan memakan waktu yang lama. Penelitian ini bertujuan untuk menyediakan rangkuman atas ulasan mobile Apps tersebut dengan pendekatan peringkasan teks secara otomomatis.  Algoritma yang digunakan dalam peringkasan teks di penelitian ini ialah Maximum Marginal Relevance (MMR) dan proses evaluasi menggunakan presisi, recall dan F1. Ulasan mobile apps diperoleh dari play store dan App Store. Ulasan akan melalui tahapan text pre-processing dengan bantuan library NLTK. Penelitian ini berhasil mengidentifikasi 30 review dengan nilai MMR tertinggi. Lebih lanjut, rangkuman ulasan yang disajikan merupakan rangkaian 10 ulasan dengan nilai MMR tertinggi. Rangkuman yang dihasilkan memiliki tingkat presisi sebesar 30.51%, recall sebesar 56.25%, dan skor F1 sebesar 39.56%.
Analysis of New 4D Hyperchaotic Systems MACM and Implementation Using Runge-kutta Abdullah, Taufik; Aqila, Aqila; Karo Karo, Ichwanul Muslim
Sains, Aplikasi, Komputasi dan Teknologi Informasi Vol 5, No 1 (2023): Sains, Aplikasi, Komputasi dan Teknologi Informasi
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jsakti.v5i1.11043

Abstract

In general, hyperchaotic system is defined as a chaotic system with more than one positive Lyapunov exponent, this implies that its chaotic dynamics extend in several different directions simultaneously. The discovery of reliable and effective methods for solving hyperchaotic systems is carried out extensively. The aim of this research is to implement nonlinear hyperchaotic using runge-kutta. The NHS system is built by inspecting, modifying and adding one state to the MACM chaotic system. In this study, we use the same initial conditions x_0=y_0=z_0=w_0=1 and the parameters a = 2,b = 2,c = 0.5, and d = 14.5. At first we experiments modified approaches, namely Classic Runge-Kutta Method 3 Order, Runge-Kutta Method Based On Arithmetic Mean, Metode Runge-Kutta Method Based On Geometric Mean, Modified Runge-Kutta Geometric Mean Based Method-1, RKLCM Approaches Instead of RKGM using MAPLE software as a calculation tool. In this work, we observe that the Runge-Kutta Method Based on Arithmetic Mean has the smallest error value with an average variable x of 0.000606953, variable y of 0.009280181, variable z of 0.000378639 and variable w of 0.60781585. Then also in RKLCM Approaches Instead of RKGM it has the largest error value with an average variable x of 0.483789793, variable y of 0.620803849, variable z of 0.92097139, and variable w of 0.596137549.
Klasifikasi Penderita Diabetes menggunakan Algoritma Machine Learning dan Z-Score Karo Karo, Ichwanul Muslim; Hendriyana, Hendriyana
Jurnal Teknologi Terpadu Vol 8 No 2 (2022): Desember, 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; Karo Karo, Ichwanul Muslim; Dewi, Sri
Jurnal Teknologi Terpadu Vol 8 No 2 (2022): Desember, 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.
Comparison of Activation Functions on Convolutional Neural Networks (CNN) to Identify Mung Bean Quality Karo Karo, Ichwanul Muslim; Karo Karo, Justaman Arifin; Ginting, Manan; Yunianto, Yunianto; Hariyanto, Hariyanto; Nelza, Novia; Maulidna, Maulidna
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13107

Abstract

Mung bean production levels by farmers in Indonesia are not stable. When there is a surplus, the stock of mung beans in the warehouse will accumulate, the storage factor affects the quality of mung beans. Indicators of quality mung beans can be seen from the color and size through direct observation. However, the aspect of view and assessment and the level of health of each observer is a human error in the classification of mung bean quality so that the results are less than optimal. One alternative way to identify object quality is to use deep learning algorithms. One of the popular deep learning algorithms is convolution neural network (CNN). This study aims to build a model to classify the feasibility of mung beans. The process of building the model also goes through the image preprocessing stage. In the process of building the model, there are ten setup parameters and four setup data used to produce the best model. As a result, the best CNN algorithm model performance is obtained from data setup I, with accuracy, precision, recall and F1 score above 75%. In addition, this study also analyzes Rel U and Adam activation functions on CNN algorithm on model performance in identifying mung bean quality. CNN algorithm with Adam activation function has 92% accuracy, 92.53% precision, 91.9% recall, and 92.19% F1 score. In addition, the performance of CNN algorithm with Adam activation function is superior compared to CNN algorithm with Adam activation function and previous study
Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture Karo Karo, Ichwanul Muslim; Kiswanto, Dedy; Panggabean, Suvriadi; Perdana, Adidtya
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.13110

Abstract

Hair diseases are common and can be caused by a variety of factors, including genetics, stress, nutritional deficiencies, as well as exposure to sunlight and air pollution. Accurate diagnosis of hair diseases is important for proper treatment, but can be challenging due to overlapping symptoms. The development of the healthcare world has widely utilized machine learning and deep learning approaches to assist in the healthcare field. This research aims to develop hair disease classification using Convolutional neural network (CNN). The CNN-based approach is expected to help health professionals diagnose hair diseases accurately and provide targeted treatment. This research involves an experimental design with three main stages: identifying the research problem, conducting a literature review, and collecting data. The research uses a dataset of hair disease images obtained from Kaggle, which are annotated and organized based on different hair disease types. After the image data is collected, the image dataset will go through the image preprocessing stage. Experiments were conducted using hair disease image data with 15 epochs on a CNN Deep Learning model with VGG-16 architecture, and resulted in an accuracy of 94.5% and a loss rate of 18.47%, with a testing epoch time of 9 hours 48 minutes. The results of this study show that CNN with VGG-16 architecture can successfully classify 10 types of hair diseases
Klasifikasi Mutu Fisik Tempe Menggunakan Metode Convolutional Neural Network (CNN) Karo Karo, Ichwanul Muslim; Karo Karo, Justaman Arifin; Yunianto, Yunianto; Hariyanto, Hariyanto; Falah, Miftahul
STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Vol 8, No 2 (2023)
Publisher : Universitas Indraprasta PGRI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/string.v8i2.17596

Abstract

The quality of tempeh has until now been determined through direct physical observation. The results of observations frequently show less consistency due to human visual limitations. Image processing is an alternative used to determining the quality of tempeh from the image aspect. Image processing has capabilities that are more sensitive, precise, and objective than human vision. Convolutional Neural Network (CNN) is a deep learning model that is able to identify image objects in such a way as to determine the type of the object. In some cases, CNN algorithm is used to identify the condition of an object quality. This research aims to identify the quality of tempeh from the image aspect to ensure whether the tempeh can be classified as the tempeh having good condition or the one starting to decompose. The image of tempeh is primary data obtained directly from one of the traditional markets in Medan. The number of images that were successfully obtained was 262. The resulted classification model went through seven phases: data preparation, preprocessing, data augmentation, dataset splitting, building a classification model with the CNN algorithm with the ReLU activation function, model testing, and evaluation. The results show that the model generated from 80% of the data has an accuracy of 98.71% and a loss rate of 0.0433%. In conclusion, this study shows that the loss rate will stabilize at this rate after 50 epochs.
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