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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika UNIKA Santo Thomas Informatika Jurnal Tekinkom (Teknik Informasi dan Komputer) MEANS (Media Informasi Analisa dan Sistem) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Applied Data Sciences Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA)
Munthe, Ibnu Rasyid
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PENGGUNAAN METODE TOPSIS DALAM SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN JENIS USAHA WISATA DI LABUHAN BATU Simamora, Rikardo Lasroha; Munthe, Ibnu Rasyid; Sihombing, Volvo
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.962

Abstract

The development of the tourism sector is an important focus in driving local economic growth. In this context, choosing the right type of tourism business is a strategic step in maximizing regional potential. This study aims to investigate the use of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) Method in the development of a Decision Support System (DSS) for selecting types of tourism businesses in Labuhan Batu. The initial stage of the research involved analyzing the criteria for selecting the type of tourism business which included aspects of market potential, sustainability, social impact, and infrastructure. The ranking results provide a guide in selecting the type of tourism business that has the highest potential to develop in Labuhan Batu. Sensitivity analysis of changes in criteria weight provides further insight into the effect of weight on alternative rankings. The use of computer technology in this study allows the development of an interactive Decision Support System capable of providing recommendations based on the results of TOPSIS calculations. The conclusion from this study is that the TOPSIS method can be an effective tool in assisting decision makers in choosing the type of tourism business that is in accordance with the characteristics of the Labuhan Batu area.
Optimalkan Bisnis Lokal (UMKM) Desa Gunung Selamat di Google Maps Munthe, Ibnu Rasyid; Masrizal, Masrizal; Rambe, Bhakti Helvi; Febriyanti, Ade Eka; Gaja, Eggi Ok Pernanda; Tompul, Fahreza Br; Sihite, Holong Marhula; Aziz, Tengku Irwan; Harahap, Tongku Hamonangan
JURNAL PKM IKA BINA EN PABOLO Vol 4, No 1: PENGABDIAN KEPADA MASYARAKAT | JANUARI 2024
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/ikabinaenpabolo.v4i1.5332

Abstract

Usaha, Mikro, Kecil, dan Menengah (UMKM) memainkan peran penting dalam pembangunan ekonomi Indonesia. Meskipun jumlah UMKM di Indonesia cukup besar dan memiliki potensi untuk menyerap tenaga kerja, sebagian besar dari mereka belum mengadopsi teknologi digital secara maksimal dalam operasional bisnis mereka. Hal ini mengakibatkan potensi keuntungan yang diperoleh belum teroptimalkan. Dalam era digital, kemampuan memanfaatkan teknologi menjadi kunci dalam bertahan di pasar yang semakin kompetitif. Generasi muda sebagai agen perubahan yang harus aktif dalam mengatasi masalah ini. Mereka dapat berpartisipasi dalam kegiatan pengabdian kepada masyarakat untuk meningkatkan pemahaman dan penerapan teknologi digital, khususnya dalam pemasaran, terutama bagi pelaku UMKM. Pendampingan dan pelatihan terkait teknologi digital menjadi penting dalam meningkatkan daya saing UMKM di pasar global yang semakin terhubung secara digital. Pengabdian kepada masyarakat ini dilakukan di Desa Gunung Selamat, Kecamatan Bilah Hulu, Kabupaten Labuhan Batu. Metode yang digunakan adalah pendekatan kualitatif dengan fokus pada pendaftaran lokasi usaha UMKM di Google Maps. Melalui program pendampingan, pemilik UMKM diajari langkah-langkah pendaftaran, pengelolaan profil bisnis, dan manfaat teknologi digital dalam pemasaran. Program ini diharapkan membantu UMKM untuk lebih eksis di pasar global dan meningkatkan daya saing mereka. Hasil dari kegiatan ini menunjukkan bahwa pelaksanaan pendaftaran lokasi usaha di Google Maps berjalan sukses. Program pendampingan ini mampu memberikan pemahaman baru kepada pemilik UMKM tentang manfaat teknologi digital dalam mengembangkan usaha mereka. Selain pendaftaran, program ini juga mendukung UMKM dalam mengurus legalitas usaha dan akun WhatsApp Business. Kepala Desa Gunung Selamat juga memberikan dukungan positif dalam pelaksanaan kegiatan. Kesimpulannya, program pendampingan pendaftaran lokasi usaha di Google Maps di Desa Gunung Selamat adalah langkah positif untuk membantu UMKM mengatasi kendala pemasaran. Program ini diharapkan dapat meningkatkan pemahaman dan pemanfaatan teknologi digital, sehingga UMKM dapat bersaing lebih efektif di pasar global. Peran generasi muda dalam membantu UMKM mengadopsi teknologi digital juga menjadi kunci dalam pertumbuhan ekonomi lokal dan nasional.
PELATIHAN BASIC CYBER SECURITY UNTUK KEAMANAN DAN PERLINDUNGAN DATA PRIBADI DI DUNIA DIGITAL Juledi, Angga Putra; Nasution, Marnis; Harahap, Syaiful Zuhri; Irmayani, Deci; Munthe, Ibnu Rasyid
JURNAL PKM IKA BINA EN PABOLO Vol 4, No 2: PENGABDIAN KEPADA MASYARAKAT | JULI 2024
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/ikabinaenpabolo.v4i2.6045

Abstract

Pencurian data pribadi melalui layanan yang membutuhkan data pribadi telah menjadi isu yang marak dibicarakan. Sebagai negara hukum, Indonesia wajib melindungi hak asasi manusia sesuai dengan konstitusi dan undang-undang yang berlaku, namun kurangnya pemahaman masyarakat tentang perlindungan data pribadi serta langkah-langkah keamanan yang tepat sering menjadi penyebab utama terjadinya kejahatan siber.Program Pengabdian Masyarakat oleh dosen Fakultas Sains dan Teknologi Universitas Labuhanbatu di SMK 2 Rantau Utara bertujuan untuk memberikan pemahaman mengenai pentingnya melindungi data pribadi dari ancaman dunia siber. Kegiatan ini mencakup edukasi tentang konsep dasar keamanan cyber, penggunaan fitur backup, pengelolaan Personal Identification Number (PIN), penerapan Two-Factor Authentication (2FA), dan mengenali serta memahami penipuan digital. Selain meningkatkan kesadaran dan literasi digital, kegiatan ini juga memberikan solusi teknis untuk melindungi data pribadi agar terhindar dari ancaman keamanan informasi. Dengan pendekatan yang melibatkan berbagai pihak, diharapkan masyarakat dapat lebih bijaksana dalam menjaga dan melindungi data pribadi mereka di era digital.
Penyuluhan Etika dan Attitude Bermedia Sosial di Usia Remaja Pada Tingkat Sekolah Menengah Atas Harahap, Syaiful Zuhri; Juledi, Angga Putra; Munthe, Ibnu Rasyid; Nasution, Marnis; Irmayani, Deci
JURNAL PKM IKA BINA EN PABOLO Vol 3, No 2: PENGABDIAN KEPADA MASYARAKAT | JULI 2023
Publisher : IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/ikabinaenpabolo.v3i2.4721

Abstract

Perkembangan teknologi informasi dan komunikasi yang revolusioner telah membawa dampak besar pada masyarakat, terutama dengan kemunculan media sosial sebagai platform kuat yang membentuk komunikasi dan interaksi sosial. Remaja di usia Sekolah Menengah Atas juga terpengaruh oleh fenomena ini, di mana media sosial menjadi bagian tak terpisahkan dari kehidupan mereka. Melalui media sosial, remaja memiliki kesempatan untuk menyuarakan pendapat, memperluas jaringan sosial, dan membentuk koneksi dengan dunia di sekelilingnya. Namun, media sosial bukan hanya sekadar wadah untuk berkomunikasi dan berkreativitas, tetapi juga menjadi panggung bagi remaja untuk mengadvokasi isu-isu sosial yang mereka pedulikan. Namun, ada potensi risiko serius yang terkait dengan penggunaan media sosial oleh remaja. Salah satu tantangan utamanya adalah penyebaran berita palsu atau hoaks yang dapat dengan mudah menyebar dan memicu kebingungan di kalangan remaja. Hal ini dapat mempengaruhi persepsi mereka tentang isu-isu global. Selain itu, perundungan cyber juga menjadi ancaman serius bagi remaja di dunia maya, yang dapat meninggalkan bekas trauma emosional dan membuat remaja merasa terisolasi dalam kesendirian. Kecanduan media sosial juga menjadi masalah serius yang dapat mempengaruhi kesehatan fisik dan mental mereka. Untuk mengatasi tantangan ini, penting bagi pihak-pihak terkait, seperti institusi pendidikan, orang tua, dan guru, untuk bersinergi dan mencari solusi yang tepat. Edukasi tentang etika bermedia sosial harus diintegrasikan dalam kurikulum sekolah dan orang tua harus terlibat aktif dalam mengawasi aktivitas online remaja. Kampanye kesadaran tentang etika bermedia sosial juga dapat diadakan untuk menciptakan budaya positif di dunia maya. Hasil dari kegiatan penyuluhan diharapkan dapat meningkatkan kesadaran remaja tentang pentingnya berperilaku etis dan positif di dunia maya. Perubahan sikap dan perilaku positif diharapkan terjadi, sehingga remaja dapat menggunakan media sosial secara lebih bertanggung jawab dan menghindari risiko negatif yang terkait dengannya. Dengan demikian, generasi muda akan menjadi lebih cerdas dan tanggap dalam berinteraksi di dunia maya, menciptakan lingkungan digital yang sehat dan aman bagi semua pengguna.
Implementation of Support Vector Machine Algorithm for Shopee Customer Sentiment Anlysis Sitepu, Melda Betaria; Munthe, Ibnu Rasyid; Harahap, Syaiful Zuhri
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11408

Abstract

As the number one largest marketplace in Indonesia based on the criteria for the origin of international stores, Shopee must always improve the quality of its products and services based on reviews from users. Given the huge number of user reviews, it is not effective to identify them by reading one by one. For this reason, an automated system is needed that can read and identify reviews better. Sentiment analysis has proven to do the job. This study aims to conduct a sentiment analysis of shopee product reviews from users who use English. This study applies the Support Vector Machine algorithm to classify the Shopee user review data. To solve this problem, the research was carried out by going through several stages, namely: pre-processing the text of the dataset, performing feature extraction, after that the word weighting was carried out using the TF-IDF method, after clean data was obtained, the SVM algorithm was implemented, for further evaluation of the model. In the results of the study, it was found that the word that most represented the positive opinion of Shopee customers was "Good" with a total of 4684 words. While the word that represents the most negative opinion is "Seller" with 68 words. From the five sentiment analysis models tested, the average value of the confusion matrix is ​​obtained, which are precision=1, recall=0.97, and f1-score=0.98. From this research, it can be concluded that the SVM algorithm is proven to be applicable in conducting sentiment analysis on user reviews of Shopee products with an average accuracy rate of 97.3%.
Implementation of Exploratory Data Analysis and Artificial Neural Networks to Predict Student Graduation on-Time Muliani, Sonia Sri; Sihombing, Volvo; Munthe, Ibnu Rasyid
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13658

Abstract

Almost all universities in Indonesia face the problem of a low number of students graduating on time. This will affect higher education accreditation. For this reason, universities must pay attention to the timely graduation of their students. The way that can be taken is to predict students' graduation on time. This research aims to predict students' timely graduations using a combination of exploratory data analysis and artificial neural networks. Exploratory data analysis is used to study the relationship between features that influence students' on-time graduation, while artificial neural networks are used to predict on-time graduation. This research goes through method stages, starting with determining the dataset, exploratory data analysis, data preprocessing, dividing training and test data, and applying artificial neural networks. From the research, it was found that Work features and GPS features greatly influence graduation on time. Students who study while working are less likely to graduate on time compared to students who do not work. Students who have an average GPS above 3.00 for eight consecutive semesters will find it easier to graduate on time. Meanwhile, Age and Gender features have no effect on graduating on time. With a percentage of 50% training data and 50% test data, epoch 100, and learning rate 0.001, the best network model was obtained to predict graduation on time with an accuracy rate of 69.84%. The research results also show that the amount of test data and the learning rate can influence the level of accuracy. Meanwhile, the number of epochs does not affect the level of accuracy.
A Comparative Analysis of Machine Learning Algorithms for Predicting Paddy Production Aditya, Nanda; Munthe, Ibnu Rasyid; Sihombing, Volvo
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13666

Abstract

For countries with large populations, such as Indonesia, food security is a very important issue. The majority of Indonesia's population depends on rice as their main food, and paddy is one of the most widely cultivated food commodities. The very good and accurate national paddy production prediction results really support decisions regarding national paddy production targets for the coming period. Therefore, to ensure supply and price stability, paddy availability must be predicted. Many studies have used machine learning to predict crop yields. By learning important patterns and relationships from input data, machine learning can combine the advantages of other methods to make better predictions of paddy yields. The aim of this research is to conduct a comparative analysis between three machine learning algorithms, namely, random forest, decision tree, and k-nearest neighbors, in predicting paddy production. To determine which algorithm is the best, a model evaluation is carried out using the coefficient of determination (R2-score), mean absolute error (MAE), and mean squared error (MSE). This research goes through methodological stages, starting from collecting datasets, data preprocessing, training and testing split datasets, applying algorithms, and evaluating the model. From this research, results were obtained for the random forest algorithm with an R2-score of 82.38%, MAE of 261726.20, and MSE of 2.19495E+11. For the decision tree, the R2-score was 79.62%, MAE was 323257.99, and MSE was 2.49304E+11. Meanwhile, k-nearest neighbors obtained an R2-score of 76.25%, MAE of 318433.42, and MSE of 2.90577E+11. The results of this research show that the random forest algorithm is the best for predicting paddy production because it obtains a larger R2-score as well as smaller MAE and MSE results.
Pengimplementasian Tingkat Ketepatan Waktu Kelulusan Siswa (Studi Kasus Di MTS Nur Ibarhimy) Menggunakan Algoritma C4.5 Amansyah, Rizky; Masrizal, Masrizal; Munthe, Ibnu Rasyid
Jurnal Informatika Vol 12, No 2 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i2.5767

Abstract

Education has a very important role in shaping the individual and directing the development of society. As an educational institution, MTS Nur Ibrahimy has a responsibility to improve the quality and efficiency in the implementation of Education. MTS Nur Ibrahimy is located in Rantauprapat, Rantau Selatan district, Labuhanbatu Regency. MTs Nur Ibrahimy has been established since 2000 and has produced a number of students who successfully completed their education at this school. Along with technological advances, pattern exploration can be done by using data classification techniques obtained through the data mining process. Data mining is generally done because of the large amount of data, which can be used to generate patterns and useful knowledge in the business operations of a company. One of the methods developed in data mining is a way to dig up existing data to build a model, and then use the model to recognize other data patterns that are not contained in the stored database. In this context, a classification model is created to identify data patterns related to "Passed" or "not passed" status classes, based on pattern Determination results from training data. The Decision Trees Model is an implementation of the classification model in data mining. This Model builds a decision tree from training data consisting of records in a database. The C4.5 algorithm is one of the data classification algorithms that uses decision tree techniques and is able to manage numerical (continuous) and discrete data, and can handle missing attribute values. This algorithm produces rules that are easy to interpret. C4.5 has been tested in various classification cases, including in medical, trade, personnel, and various other fields.
Optimisasi Penilaian Kinerja Karyawan PT. Tolan Tiga Indonesia Estate Perlabian Dengan Algoritma C4.5 Sipahutar, Rizka Nurfatni; Munthe, Ibnu Rasyid; Juledi, Angga Putra
Jurnal Informatika Vol 12, No 2 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i2.5677

Abstract

In the face of fierce competition in the current global era, companies are required to prepare and always adjust their strategies to the changes that occur so that the company remains able to compete and survive. Employees who are in a company are workers which is the most important asset that must be owned and indispensable in the company and of course must be considered by all parties in order to create good performance as well as have goals to be achieved in the assessment of employee performance at PT. There Are Three Types Of Indonesian Real Estate. Employee performance appraisal in the company is seen as the driving force of the company because human resources play an active role in the running of an organization or company and the decision-making process. Machine learning tools used in predicting the assessment, using the C4.5 algorithm, the data obtained is more accurate. Machine learning is an artificial intelligence that can process data that is useful for consideration in making decisions and solving problems. C4.5 algorithm is one of the algorithms in data mining that serves to classify a class. This algorithm is a development of the ID3 algorithm. How the C4.5 algorithm works by forming a decision tree to produce a decision.
Implementation of Stacking Technique Combining Machine Learning and Deep Learning Algorithms Using SMOTE to Improve Stock Market Prediction Accuracy Munthe, Ibnu Rasyid; Rambe, Bhakti Helvi; Hanum, Fauziah; Amanda, Ade Trya; Hutagaol, Anita Sri Rejeki; andrianto, Richi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.421

Abstract

This study introduces a stacking technique that integrates machine learning (ML) and deep learning (DL) algorithms to enhance the accuracy of stock market trend predictions. The stacking model utilizes XGBoost and Random Forest as base models from the ML domain, while Logistic Regression and LSTM (Long Short-Term Memory) function as meta models to optimize predictive accuracy. A significant challenge in stock market data is class imbalance, where certain trends, such as stock price drops, are underrepresented. To mitigate this, we applied the Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic data for the minority class. This approach helps the model better capture patterns from the underrepresented data while preserving essential information from the majority class. The implementation of SMOTE, coupled with the stacking technique, yielded a substantial improvement in prediction accuracy. The results showed that the Random Forest algorithm achieved an accuracy of 85% with precision, recall, and F1-score all at 85%, while XGBoost and Logistic Regression achieved accuracies of 82% and 81% respectively. For the deep learning models, LSTM reached an accuracy of 83%, while the Stacking Meta Model with LSTM achieved an accuracy of 83% with slightly better precision and recall at 84%. The stacking model, with Logistic Regression as the meta model, ultimately achieved the highest accuracy of 86%, outperforming individual models such as SVM (Support Vector Machine), LSTM, Random Forest, and Logistic Regression (LR). These findings demonstrate the efficacy of combining SMOTE with stacking to address data imbalance and improve stock market predictions. The novelty of this study lies in the integration of advanced ML and DL models within a stacking framework to handle class imbalance in financial datasets. Future research will explore the deployment of this model in a real-time web-based application to support investor decision-making in stock market trend analysis.
Co-Authors Aditya, Nanda Ah, Rahma Muti Amanda, Ade Trya Amansyah, Rizky Anita Sri Rejeki Hutagaol, Anita Sri Rejeki Aritonang, Yuliana Br Azhari Lubis, Haldina Syahfitri Aziz, Tengku Irwan Fauziah, Ridha Febriyanti, Ade Eka Fitriandika Sari, Novi Gaja, Eggi Ok Pernanda Gulo, Nur'Ainun Hanum, Fauziah Harahap, Tongku Hamonangan Harist N, Abdul Hasibuan, Mila Nirmala Sari Helvi Rambe, Bhakti Ichlas, Al Irmayani Irmayani Irmayani, Deci Juledi, Angga Putra Lashiyanti, Allbila Rahajeng Lumban Gaol, Tid Verawati Maulam, Zevi Adam Muhammad Halmi Dar Muliani, Sonia Sri Mutia Mutia, Mutia Nasution, Fitri Aini Nasution, Marnis Nurul Pratiwi Pane, Dinda Nurinayah Pane, Rahmadani Pertiwi, Nur Fajar Kurnia Petromak Sinaga, Gandhi Naek Putri, Kirana Dwi Rambe, Bhakti Helvi Rasyid Munthe, Shabrina Richi Andrianto Ritonga, Irna Alfaini Rusmiana, Rusmiana SANDI ARDIANSYAH Sari, Miranda Sari, Novi Fitriandika Sihite, Holong Marhula Sihombing, Volvo Simamora, Rikardo Lasroha Sipahutar, Rizka Nurfatni Siregar, Siti Kholijah Sitepu, Melda Betaria Sitompul, Joni Awendri Sitorus, Anggiat Selamat Suci Ramadhani, Suci Syaiful Zuhri Harahap Syaputra, Panca Tiara, Dewi Tompul, Fahreza Br Tonny, Tonny Trya Amanda, Ade Wiranti, Dea