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All Journal Infotech Journal Sinkron : Jurnal dan Penelitian Teknik Informatika IT JOURNAL RESEARCH AND DEVELOPMENT INTECOMS: Journal of Information Technology and Computer Science KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Teknik dan Informatika Building of Informatics, Technology and Science Jurnal Mantik Jurnal Sains dan Teknologi Community Engagement and Emergence Journal (CEEJ) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) INFOKUM Jurnal Darma Agung Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) International Journal Of Science, Technology & Management (IJSTM) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) KLIK: Kajian Ilmiah Informatika dan Komputer Instal : Jurnal Komputer Jurnal Info Sains : Informatika dan Sains Bulletin of Information Technology (BIT) Jurnal Fokus Manajemen Jurnal Minfo Polgan (JMP) Jurnal Nasional Teknologi Komputer Jurnal Pengabdian Masyarakat Gemilang (JPMG) Data Sciences Indonesia (DSI) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Best Journal of Administration and Management Jurnal INFOTEL Bulletin of Engineering Science, Technology and Industry
Sitorus, Zulham
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SENTIMENT ANALYSIS OF INDONESIAN COMMUNITY TOWARDS ELECTRIC MOTORCYCLES ON TWITTER USING ORANGE DATA MINING Sitorus, Zulham; Saputra, Maulian; Sofyan, Siti Nurhaliza; Susilawati
INFOTECH journal Vol. 10 No. 1 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i1.9374

Abstract

This study explores sentiment analysis of the Indonesian community towards electric motorcycles on Twitter using Orange Data Mining. In the context of the increasing popularity of electric vehicles, especially electric motorcycles, understanding public sentiment becomes crucial for various stakeholders. Twitter, as a leading social media platform, serves as a rich source of opinions and discussions on various topics, including electric motorcycles. This research utilizes Orange Data Mining with multilingual sentiment analysis techniques to analyze the sentiment of the Indonesian community regarding electric motorcycles. The results of sentiment analysis are visualized through box plots and scatter plots, aiming to classify Twitter users based on their emotional responses. The findings of this study provide valuable insights into the sentiment landscape surrounding electric motorcycles in Indonesia, benefiting policymakers, manufacturers, and marketers in understanding public perception and making informed decisions.
Implementation of K-Means Clustering for Inventory Projection Sitorus, Zulham; Syahputra, Irwan; Indra Angkat, Chairul; Sartika, Dewi
International Journal of Science, Technology & Management Vol. 5 No. 3 (2024): May 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i3.856

Abstract

Inventory forecasting is crucial in effective supply chain management and cost reduction. However, traditional forecasting techniques face significant challenges due to the complexity and variability of demand patterns. This study explores the use of K-means clustering, a data-driven approach that can improve inventory forecasting accuracy. By grouping inventory items based on their unique demand profiles, we can create personalized forecasting models for each cluster. This technique enhances demand estimation, helping businesses make informed decisions and optimize their inventory. Our research delves into the use of K-means clustering to identify patterns and similarities within historical demand data. This clustering process divides inventory items into groups with similar demand characteristics. By applying specific forecasting models to each cluster, we achieve greater precision in our predictions. The proposed methodology is rigorously evaluated using real-world inventory datasets, and the results demonstrate its significant superiority in forecasting accuracy compared to conventional non-clustered methods. This study offers compelling evidence and valuable insights for practitioners seeking to improve their inventory management practices through data-driven techniques.
Analysis Of Indonesian People's Sentiment Towards 2024 Presidential Candidates On Social Media Using Naïve Bayes Classifier and Support Vector Machine Mardiah, Nia; Marlina, Leni; Khairul, Khairul; Sitorus, Zulham; Iqbal, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5766

Abstract

This research aims to analyze the sentiment of the Indonesian public towards the 2024 presidential candidates on social media platforms X and Instagram. The main issue addressed is how to determine public opinion as disseminated on social media regarding the presidential candidates. To address this issue, two classification methods are used: Naïve Bayes Classifier and Support Vector Machine (SVM). The objective of this research is to measure public sentiment, both positive and negative, towards the 2024 presidential candidates using these two methods. The research findings indicate that the implementation of the Naïve Bayes method with manual labeling achieved the highest accuracy of 86% for X data and 85% for Instagram comments data. Meanwhile, with lexicon-based labeling, the highest accuracy was 60% for both X and Instagram data. The SVM method with manual labeling also achieved the highest accuracy of 86% for X data and 85% for Instagram data. With lexicon-based labeling, the highest accuracy was 60% for X data and 70% for Instagram data. This research concludes that both Naïve Bayes and SVM demonstrate strong performance in sentiment analysis on social media, with SVM slightly outperforming in some scenarios. The implementation of these two methods provides valuable insights into public opinion towards the 2024 presidential candidates on social media.
Pemanfaatan Teknologi Virtual Reality (VR) Dalam Pembelajaran Pada Lembaga Kursus Dan Pelatihan Rumah Tik Labuhanbatu Ernawati, Andi; Sitorus, Zulham; Wijaya, Rian Farta; Aulia, Ananda; Siregar, Andree Risky Yuliansyah; Sofyan, Siti Nurhaliza
Jurnal Pengabdian Masyarakat Gemilang (JPMG) Vol. 4 No. 1 (2024): Januari 2024
Publisher : HIMPUNAN DOSEN GEMILANG INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58369/jpmg.v4i1.152

Abstract

Learning using technology continues to develop along with developments in technology itself. One technology that is increasingly being used in learning is virtual reality (VR). Virtual Reality is a technology that produces a digital environment that resembles the real world and allows users to interact with that environment. Including in Labuhanbatu Regency, where virtual reality media has been used as a learning medium. However, if we observe that the use of virtual reality as a learning medium has not been distributed thoroughly to every level of student, only the student level can access learning using virtual reality. And if we look more deeply, the virtual reality media content that is presented is able to improve the quality of student learning for each level because virtual reality media will really help improve the imagination and mindset of students at all levels to become more effective and efficient. In Labuhanbatu Regency there are many course and training institutions, judging from the area and the number of course and training institutions in Labuhanbatu Regency, the introduction of virtual reality learning media in Labuhanbatu Regency should be managed generally for all levels of students as a basis for community service activities (PKM). . This activity aims to provide training to students in Labuhanbatu Regency about learning methods using virtual reality technology through videos on cellphones using the Millea Lab Viewer application and VR Player with a virtual box device. Activities carried out focus on introducing and training virtual reality for learning. The methods used include training for students at all levels at the Labuhanbatu Tik House course and training institution regarding the features and concepts of virtual reality, the goals, benefits and practices of using virtual reality. After attending the training, students at the Tik Labuhabatu home course and training institution experienced an increase in their knowledge about virtual reality, as well as their ability to improve the quality and creative imagination in learning virtual reality media content in Labuhanbatu Regency Keywords: virtual reality; learning; content, lkprumahtik, Labuhanbatu
Enhancing Text Messages with a Combination of Vigenère Cipher and One Time Pad Using Random Key LFSR Ibezato Zalukhu, Anzas; Sitorus, Zulham; Suhardiansyah, Suhardiansyah; Septiani, Nadya
Jurnal Sains dan Teknologi Vol. 6 No. 1 (2024): Jurnal Sains dan Teknologi
Publisher : CV. Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/saintek.v6i1.3190

Abstract

In today's global era, the development of information systems is rapidly advancing and becoming increasingly sophisticated, with nearly all elements utilizing information and communication technology in their daily activities. One frequent activity is the transmission or exchange of data over the internet. However, this advancement also raises concerns about the security of messages or data being sent. To mitigate the risk of message misuse, cryptographic techniques can be used to maintain data or message confidentiality by encrypting the information before transmission. This research aims to combine the Vigenère cipher algorithm with a one-time pad using a random key generated by a linear feedback shift register (LFSR) method to enhance the security of text messages. The research methodology involves generating a public key for the One-Time Pad algorithm using LFSR. The encryption process is initially performed with the Vigenère cipher, and the resulting encrypted message is further encrypted using the one-time pad with a key generated by the LFSR method. This algorithm is implemented using the Visual Basic programming language. The research findings indicate that the combination of the two algorithms, with the random key generated by the LFSR for the One-Time Pad, is capable of enhancing text message security by producing random and unique ciphertexts. By using Modulo 256 and ASCII conversion, random ciphertexts can be generated, thereby reducing the likelihood of message breaches. Additionally, this research provides further insights into the process of text message encryption and decryption.
Comparison of K-Means and Self Organizing Map Algorithms for Ground Acceleration Clustering Simamora, Siska; Muhammad Iqbal; Andysah Putera Utama Siahaan; Khairul, Khairul; Zulham Sitorus
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14120

Abstract

This study evaluates earthquake-induced ground acceleration in Indonesia, which is located in the Pacific Ring of Fire zone, using Donovan's empirical method and comparing two clustering algorithms, Self Organizing Map (SOM) and K-Means. The main problem faced is the high risk of earthquakes in Indonesia and the need for effective methods to predict potential damage to buildings and infrastructure. The research objective is to evaluate earthquake-induced ground acceleration and identify acceleration distribution patterns using clustering techniques. The solution methods used include the application of the Donovan method to calculate ground acceleration based on BMKG data, as well as the use of SOM and K-Means algorithms to cluster the ground acceleration data. GIS and Python applications are used to visualize the clustering results. The results show that the Donovan method integrated with SOM and K-Means provides significant insights into the distribution of ground acceleration, thus assisting in risk evaluation, disaster mitigation planning, and the development of more effective earthquake-resistant infrastructure development strategies in Indonesia
Implementasi Sistem Pendukung Keputusan dalam menentukan Kecamatan Terbaik Menggunakan Algoritma Entropy dan Additive Ratio Assessment (ARAS) Ernawati, Andi; Ofta Sari, Ayu; Sofyan, Siti Nurhaliza; Aulia, Ananda; Sitorus, Zulham; Khairul
Bulletin of Information Technology (BIT) Vol 4 No 4: Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i4.1066

Abstract

In the context of regional development and decision making related to determining the best village, the use of a Decision Support System (DSS) with the application of the Entropy and Additive Ratio Assessment (ARAS) algorithms is a very important approach. The main objective of this research is to propose and implement a method that utilizes the Entropy algorithm to evaluate criteria weights and ARAS to rank villages based on predetermined criteria. This approach begins the process by identifying relevant criteria to determine the best village in an area. Next, the Entropy algorithm is used to measure the level of importance or relative weight of each predetermined criterion. This step helps in assessing how informative each criterion is in the decision-making process regarding determining the best Village. After determining the criteria weights using Entropy, the approach continues with the application of the ARAS method. ARAS is used to rank villages based on normalized values ​​from previously determined criteria. The data normalization process is carried out to ensure the validity of comparisons between villages. The final result of this approach is a ranking of villages indicating the best villages based on the criteria considered. This method was tested in a case study using a dataset involving a number of relevant criteria for assessing village development potential. Experimental results show that the use of the Entropy and ARAS algorithms in the Decision Support System provides an effective and informative framework for decision makers in determining the best Village. In conclusion, this approach provides a solid foundation to support a more effective and precise decision-making process in regional development based on clearly defined criteria.
Penerapan Metode Certainty Factor Pada Sistem Pakar Diagnosa Penyakit Gigi Dan Mulut Zalukhu, Anzas Ibezato; Irwan Syahputra; Suhardiansyah; Sitorus, Zulham; Khairul
Bulletin of Information Technology (BIT) Vol 4 No 4: Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i4.1102

Abstract

Gigi dan mulut merupakan organ vital yang memainkan peran penting dalam menjaga kesehatan manusia. Kelainan pada gigi dan mulut dapat menjadi pemicu penyakit lain dalam tubuh. Pentingnya menjaga kesehatan gigi dan mulut ditekankan, terutama mengingat fungsinya yang esensial dalam berbicara, menjaga bentuk wajah, dan mengunyah makanan. Sayangnya, dengan perkembangan zaman, pola makan yang tidak sehat, seperti konsumsi makanan siap saji tinggi gula, garam, dan lemak, dapat menyebabkan masalah kesehatan gigi dan mulut. Penyakit gigi dan mulut sering disebabkan oleh mikroorganisme, dan pengetahuan terbatas tentang gejala-gejala penyakit ini dapat menjadi hambatan untuk diagnosis dini. Sebagai solusi, penelitian ini mengusulkan penerapan metode certainty factor dalam sistem pakar untuk mendiagnosis penyakit gigi dan mulut. Metode ini memungkinkan evaluasi tingkat keyakinan pakar terhadap data yang dianalisis, memberikan solusi atau rekomendasi dalam situasi kompleks. Penelitian ini mengacu pada pandangan pakar dokter gigi dan mulut, yang dianggap memiliki pengetahuan dan pengalaman yang mencukupi. Sistem pakar yang diusulkan bertujuan untuk meniru proses penalaran seorang pakar dalam memecahkan masalah spesifik dalam bidang gigi dan mulut. Dengan memanfaatkan certainty factor, sistem ini dapat menyediakan solusi yang lebih dapat diandalkan dan memberikan kontribusi pada upaya pencegahan serta penanganan dini penyakit gigi dan mulut.
Design and Build System Applications Design and Build Information System Applications IT Infrastructure Training and Training Iskandar, Fahmi; Sitorus, Zulham
Bulletin of Information Technology (BIT) Vol 5 No 2 (2024): Juni 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i2.1224

Abstract

To speed up IT infrastructure training for employees in the information technology department, PT. Pelabuhan Indonesia (Persero) Regional 1 Belawan Branch needs to change its current conventional training management to information technology based. This is needed because the large number of employees and large areas require efficiency in organizing training. The proposed solution is the development of a web-based information system using PHP and MySQL to make it easier to manage training and access information for organizing staff wherever they are. Web-based IT Infrastructure Training uses a PHP program with a MySQL Database so it is hoped that this information system can help the Administration Section staff to easily access information about training from anywhere and at any time using internet facilities
Application of the C45 Algorithm to Predict Student Academic Scores Ernawati, Andi; Sitorus, Zulham; Aulia, Ananda; Ayu Ofta
Bulletin of Information Technology (BIT) Vol 5 No 2 (2024): Juni 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i2.1251

Abstract

Student grades are the results of teaching and learning activities on a campus. So you can know your target for completing your studies. This research uses the C4.5 Algorithm which can help predict the results of student assessments. The dataset consists of student achievement index, place of residence, discipline, lecturer's role in lectures. From 40 datasets we have obtained a decision on student academic achievement and obtained performance results from accuracy results of 86.36% with class precision predicate Yes=84.62%, No=88.89% and class recall Yes=91.67%, No=80.00%.
Co-Authors , Arpan , Fery Anugerah A.A. Ketut Agung Cahyawan W Abdul Karim Afrizal, Sandi Akbar Maulana, Taufik Aldi Kesuma Alvian Alvian Ami Abdul Jabar Andi Ernawati Andysah Putera Utama Siahaan Angkat, Chairul Indra Antoni, Robin Ardya, Dwika Arief, Muhammad Arif Rahman Asyahri Hadi Nasyuha Aulia, Ananda Ayu Ofta Bambang Sugito Batubara, Supina Boy Rizki Akbar Br Tarigan, Sella Monika Chelfina Utami Daniel Happy Putra Danu Wardhana Azhari Darmeli Nasution DEWI SARTIKA diansyah, Suhar Eko Hariyanto Eko Hariyanto Eko Hariyanto Fahmi Iskandar Fahmi Kurniawan Farta wijaya, Rian Faza Wardanu Damanik, Dwi Gilang Ramadhan Gultom, Ananda Christianto H. Aly, Moustafa Hafiz Rodhiy Haliza, Siti Nur Hamzah, Iswadi Hartono Sinambela, Sugi Helmy, Ahmad Hendra Harnanda Heni Wulandari Hrp, Abdul Chaidir Ibezato Zalukhu, Anzas Ika Devi Perwitasari Indra Angkat, Chairul IQBAL , MUHAMMAD Irwan Syahputra Irwan Syahputra, Irwan Izhari, Fahmi Khairul Khairul Khairul, Khairul Kiki Artika Kurniawan, Fahmi Laila Maghfirah Larius Ambasador Parlindungan Leni Marlina Leni Marlina Limbong, Yohannes France M Imam Santoso M. Rasyid M.Rizki Khadafi Mardiah, Nia Marzuki Sianturi, Ismail Melva Sari Panjaitan Meri Sri Wahyuni Mhd Arie Akbar Mohammad Yusuf, Mohammad Muhammad Fahriza Muhammad Iqbal Muhammad Iqbal Muhammad Wahyudi Nahampun, Natalia Nainggolan, Andreas Ghanneson Nasution, Darmeli Nazar Saputra, Risfan Nelviony Parhusip Nurwijayanti Ofta Sari, Ayu Parhusip, Nelviony Pranoto, Sugeng Putra, Khairil Ragil Satya Adi W Ramadani, Pebri Ramadhan, Aditya Ramadhan, Deni Ramadhani, Aditya Rian Farta Wijaya Rian Putra, Randi Rika Uli Samosir, Siska Risky, Raihan Rusydi Tanjung , Miftah Sahputra, Fajar Said Oktaviandi Saputra, Maulian Sari Penjaitan, Melva Septiani, Nadya Sianturi, Ismail Sibarani, Dina Marsauli Simamora, Siska Simbolon, Fikri Zuhaili Simorangkir, Elsya Sabrina Asmita Sinambela, Sugi Hartono Sinyo Andika Nasution, Ahmad Sipra Barutu Siregar, Andree Risky Yuliansyah Sitepu, Fernando Sitinur, Siti Nurhaliza Sofyan Sitompul, Jelly Rolley Sofyan, Siti Nurhaliza Solly Ariza Lubis Sri Wahyuni, Meri Suhardiansyah Suhardiansyah Suhardiansyah Suherman Suherman Sukrianto, Sukrianto Sutiono, Sulis Syahputri, Maulisa Syamsiar, Syamsiar T, Siti Isna Syahri Tanjung, Miftah Rusydi Utama, Hendra Vina Arnita Vivin Yulfia Sarah Wahyu Agung Pratama Wahyuni, Meri Sri Wijaya, Rian Farta Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Wirda Fitriani Yahya, Susilawati Zalukhu, Anzas Ibezato Zulfahmi Syahputera Zulfahmi Zulfahmi Zulfahmi Zulfahmi