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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Mantik Informatika Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Computer Networks, Architecture and High Performance Computing International Journal of Business, Technology, and Organizational Behavior (IJBTOB) Majalah Ilmiah UPI YPTK Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT Journal of Student Development Information System (JoSDIS) Jurnal Pendidikan Indonesia (Japendi)
Syaiful Zuhri Harahap
Universitas Labuhanbatu
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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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%.
Forecasting Health Sector Stock Prices using ARIMAX Method Aprilianto, Muhammad; Hasibuan, Mila Nirmala Sari; 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.v7i2.11418

Abstract

In daily stock trading activities, stock prices can experience ups and downs. The rise and fall of stock prices occurs due to changes in supply and demand for these shares. The COVID-19 pandemic did not have a negative effect, instead it had a positive impact on stock prices in health companies. companies in the health sector experienced a fairly good profit of 10.46% in the fourth quarter of 2021. This fact made investors interested in buying shares in companies in the health sector in the hope of selling them when demand increased, resulting in doubled profits. Stock conditions continue to fluctuate every day, making investors need to pay attention and study the past data of the health sector company that will be selected before deciding to invest. Therefore, it is necessary to forecast stock prices in the health sector for the next several periods as a step in making investment decisions. The health sector companies that will be modeled are PT Kimia Farma (Persero) Tbk and PT Kalbe Farma Tbk. The method used in this study is the ARIMAX model. The test and analysis results show that based on the RMSE and MAPE values, the best model is ARIMAX(5,13) for PT Kalbe Farma Tbk shares with a MAPE value of 1% in in-sample data and 0.6% in out-sample data.
Decision Support System for Financial Aid for Underprivileged Students using the TOPSIS Method Pransiska, Apprillia Yudha; Juledi, Angga Putra; Harahap, Syaiful Zuhri
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12798

Abstract

The distribution of Financial Aid for Underprivileged Students is considered not to be on target due to the unmeasured selection process. So a decision-making system is needed that can select beneficiaries objectively. This research was conducted to assist the school in determining students who deserve this assistance. The purpose of this research is to build a decision-making system for selecting beneficiaries from poor students by applying the Technique for Order of Preference by Similarity to Ideal Solution method. The formulation of the problem is how to build a decision support system using the Technique for Order of Preference by Similarity to Ideal Solution method in selecting students who receive financial aid for underprivileged students. The method used goes through several stages, namely: determining alternatives and criteria, building a normalized decision matrix, building a weighted normalized decision matrix, determining positive and negative ideal solutions, determining the distance between ideal solutions, and determining preference values. There are 7 criteria used, namely social protection card recipients, total income, number of dependents, parental status, distance, class, and report card scores. The results showed that the highest preference value for each alternative was in alternative A3, with a score of 0.6665. While the lowest preference value is in alternative A20 with a score of 0.0719, From the results of the study, it was concluded that the Technique for Order of Preference by Similarity to Ideal Solution method can be used in making decisions on the selection of beneficiaries of poor students based on preference value rankings.
Development of Expert System Application to Detect Chicken Disease using the Forward Chaining Method Ramadan, Ahmad; Harahap, Syaiful Zuhri; Muti’ah, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12843

Abstract

Chicken is the most widely kept and consumed poultry in Indonesia. Due to the large population of poultry, a variety of diseases have also emerged, from minor diseases to diseases that can kill chickens and infect humans. As a result of these diseases, there are implications for the losses suffered by chicken farmers. Most farmers find it very difficult to identify chicken diseases due to their lack of knowledge. On the other hand, expecting treatment from a veterinarian or expert is very limited and expensive. Therefore, a system is needed that can easily help chicken farmers detect diseases in their pet chickens. This research aims to build an expert system to detect chicken diseases by applying the forward chaining method. The expert system is implemented as a web-based application. The research stages start with identifying problems, collecting data, creating a knowledge base and production rules, building applications, and getting results. The results showed that the forward chaining method provides convenience in detecting chicken diseases. This is proven by only selecting the symptoms of the disease that appear, and then the application will provide conclusions regarding what type of disease is being suffered by chickens. In addition, this application also provides information related to ways of handling and control that can be done to overcome the chicken disease. Hopefully, the results of this research can facilitate chicken farmers in identifying and handling diseases effectively and efficiently.
Chatbot Design for Interview Questions Using Neural Network Models on the CarTech Website Sihotang, Diko Pradana; Harahap, Syaiful Zuhri; Irmayanti, Irmayanti
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.v8i2.13603

Abstract

Abstract: This research focuses on analyzing interview questions using a neural network model, implemented on the CarTech website. With the main aim of optimizing the interaction between users and the system through the questions asked, this research takes an innovative step by utilizing Google Collab as a development platform. For this research, several paragraphs were carried out, namely problem scoping, data acquisition, data exploration, modeling, evaluation, and deployment. These stages were carried out so that this research could get good results, plus the integration between Google Collab and chatbot which made it possible for this research to get good results. Google Collab makes it easy to use neural network models and integrate with chatbots, enabling efficient and effective testing and deployment of models. The results of this study are quite impressive, with an accuracy of 92%, demonstrating the model's ability to process and understand interview questions with high precision. The aim of this research is not only to explore the potential of neural network models in automatically understanding questions and providing accurate responses, but also to show how this technology can be integrated into web applications to improve the quality of user interactions, making AI-based chatbots a viable solution and effective in improving user experience on the CarTech website. In conclusion, by utilizing AI you will also get good results. As in this research, AI can help analyze interview questions with neural network models.
Implementation of Data Mining to Determine Public Interest in Automatic Motorcycles Fatma, Nurul; Harahap, Syaiful Zuhri; Masrizal, Masrizal
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.v8i2.13637

Abstract

Research on public interest in automatic motorbikes was carried out with the aim of understanding the factors that influence the decision to purchase an automatic motorbike. Using data mining methods, this research applies the K-Nearest Neighbor (KNN) and Neural Network techniques to identify and analyze people's interest patterns. The data used amounted to 139 samples, of which 127 showed interest in automatic motorbikes, while 12 others showed no interest. The research process begins with data analysis, the next stage is preprocessing, which includes data cleaning, in the model design stage in data mining, two models are built: one using KNN and the other using Neural Network. These two models are designed to classify sample data based on interest in automatic motorbikes. The next stage is model testing. Test results show that both models can classify interests accurately, with most of the sample data being classified correctly. Model evaluation was carried out to measure the effectiveness and accuracy of the two methods. The evaluation results show that both models provide very good performance, with results that almost reach a perfect score. This shows that both methods, KNN and Neural Network, are very effective in classifying and predicting people's interest in automatic motorbikes based on available data. In conclusion, this research not only shows the effectiveness of KNN and Neural Network in data mining for analyzing people's interests, but also provides valuable insights for automatic motorbike manufacturers and sellers about consumer preferences.
Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4.5 Algorithm Melyani, Sri; Harahap, Syaiful Zuhri; Irmayanti, Irmayanti
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.v8i2.13651

Abstract

Research conducted to predict the incidence of stunting in toddlers, using data mining methods such as Naive Bayes and the C4.5 algorithm has been applied to analyze health data. The main aim of this research is to develop a predictive model that can identify toddlers who are at high risk of stunting, based on variables that have been collected from medical records and health surveys. The use of the Naive Bayes and C4.5 methods in this research aims to compare the effectiveness of the two methods in dealing with complex and unbalanced classification problems. This research involves a series of crucial stages starting from data selection, data pre-processing, data mining model design, data mining model testing, to method evaluation. In this study, the sample used consisted of 200 toddlers, of which 159 were diagnosed as having stunting and 41 others were not. The classification results show significant effectiveness in both methods used. The accuracy results of both methods are very encouraging, with both methods showing success rates of more than 90%. This shows that both Naive Bayes and C4.5 are very effective in identifying patterns related to the risk of stunting among toddlers. These highly accurate results not only demonstrate the power of data mining techniques in the field of public health but also provide insights that health practitioners can use to intervene earlier in at-risk populations.
Implementation of the K-Means Method for Clustering Regency/City in North Sumatra based on Poverty Indicators Wardani, Syafira Eka; Harahap, Syaiful Zuhri; Muti’ah, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13720

Abstract

Poverty has many negative effects on people's lives, such as difficulty meeting basic needs, limited access to adequate health and education services, and limited economic opportunities. North Sumatra faces significant poverty problems as one of the largest provinces in Indonesia. This requires special attention and a thorough investigation. Reducing poverty is a very important issue for the government of North Sumatra Province. Poverty-alleviation strategies can no longer be applied uniformly. Instead, it is necessary to consider all the factors that cause poverty in each region. This means that the approach that must be given to each regency or city based on its poverty level must be adjusted. To overcome this problem, clustering must be carried out to identify areas with different levels of welfare. The aim of this research is to cluster regencies and cities in North Sumatra Province using the K-means method based on poverty indicator variables. This research only uses three poverty indicators: gross regional domestic product, human development index, and unemployment rate. The optimal number of clusters is determined based on the results of the silhouette coefficient. The research method begins with dataset collection, exploratory data analysis, data preprocessing, and k-means clustering. The value k = 6 produces a silhouette coefficient of 0.4135. This research produced six regency/city clusters. Cluster 1 consists of 11 regencies and 1 city; cluster 2 consists of 1 regency and 2 cities; cluster 3 consists of 4 regencies; cluster 4 consists of 7 regencies; cluster 5 consists of 4 cities; and cluster 6 consists of 2 regencies and 1 city. The variables gross regional domestic product, human development index, and unemployment rate have a big influence on the cluster results. This will enable the government to adopt policies to tackle poverty quickly and effectively.
Implementation of the C4.5 and Naive Bayes Algorithms to Predict Student Graduation Lianah, Lianah; Harahap, Syaiful Zuhri; Irmayati, Irmayati
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13860

Abstract

This research aims to determine student graduation using two data mining methods, namely the Naive Bayes Classifier and the C4.5 Algorithm. Research stages include data analysis, data pre-processing, model design in data mining, classification results, method evaluation, and evaluation results. This research uses student data consisting of training data and testing data to evaluate the performance of the two methods in predicting student graduation based on attributes such as attendance scores, behavior scores, Final Semester Examination (UAS) scores, and report card scores. The classification results show significant differences between the two methods. The Naive Bayes Classifier produces predictions that 37 students pass and 17 students do not pass, while the C4.5 Algorithm predicts that 30 students pass and 24 students do not pass. This difference in results indicates that there are differences in the approaches of the two methods to student graduation data, with the Naive Bayes Classifier tending to provide more positive predictions than the C4.5 Algorithm. Evaluation of the performance of the method shows that the Naive Bayes Classifier has an accuracy rate of 100%, which is a perfect result, while the C4.5 Algorithm has an accuracy rate of 89%. This significant difference in evaluation results confirms that the Naive Bayes Classifier is superior in classifying student graduation compared to the C4.5 Algorithm in the context of this research. These findings can help in making decisions regarding student graduation evaluations in the future.
Co-Authors Ah, Rahma Muti Aini, Putri Aisyah Hayati Ali Akbar Ritonga Amin, Mhd. Andini, Novira Dwi Andriani, Nur Putri ANTIKA, DEWI Aprilianto, Muhammad Ardiansyah, Rizaldi Bangun, Budianto Cahya, Susilo Tiadi Christoval, Peter Dalimunthe, Annisa Putri Faradilah, Rahma Fatma, Nurul Febriyanti, Ade Eka Hanif, Khairil Hansyah, Praida Harahap, Vivi Nadenia Hasibuan, Mila Nirmala Sari Hasibuan, Muhammad Adlin Hasibuan, Taufik Molid Hidayat Hermika, Eva Ibnu Rasyid Munthe Irmayani, Deci Irmayanti Irmayanti Irmayanti, Irmayanti Irmayati, Irmayati Iwan Purnama Iwan Purnama JP, Gafar Ilyaz Juledi, Angga Putra Juwita Juwita, Juwita Laila Sari Lestari, Putri Anggraini Lianah Lianah, Lianah Listia, Bella Ayu Lubis, Nadira Jannah Adeni M, Nelvi Nurrizqi Marnis Nasution Masrizal Megawati Pasaribu Meidy Putra Panusunan Siregar Melisa Melisa Melyani, Sri Mira Handayani Siregar Mth, Sri Rezky Aprilawati Br Muhammad Halmi Dar Munthe, Ibnu Rasyid Mushtafa Haris Munandar Muti’ah, Rahma Naibaho, Restu Fauzy Nasution, Fahri Emil Afandi Nasution, Fitri Aini Nasution, Intan Baiduri Nasution, Khodijah Nasution, Marnis Novita, Rini Pane, Dinda Nurinayah Panjaitan, Nia Putri Pasaribu, Nova Tresia Patriya, Angga Prayoga Pransiska, Apprillia Yudha Priyanti Priyanti Purba, Mhd. Rafly Putra, Fasdiansyah Putri Lestari, Putri Rafika, Mulya Rahma Muti’ah Ramadan, Ahmad Ramadhani Ramadhani Rambe, Aida Zahrah Hasanati Br Rambe, Nurhayati Rambey, Khiarul Akhyar Ritonga, Akbar Pramuja Ritonga, Ali Akbar Ritonga, Irmayanti SANDI ARDIANSYAH Sari, Kurnia Tika Sigit Prasetyo Nugroho Sihotang, Diko Pradana Sirait, Roby Gusmawan Siregar, Ade Elvi Rizki Siregar, Siti Kholijah Siregar, Siti Wahdina Sitepu, Melda Betaria Sitompul, Muhammad Sofyan Surbakti, M. Aufa Nayaka Fathan Suryadi, Sudi Suryadi, Sudi Syavitri, Tiara Wardani, Syafira Eka Wijaya, Alief Achmad Yeni Syahfutri S Yenni Syahfutri Sipahutar ZURAIDAH ZURAIDAH