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All Journal JSM (Jurnal SIFO Mikroskil) Journal of Computer System and Informatics (JoSYC) Teknika Journal of Social Responsibility Projects by Higher Education Forum Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Heru Kurniawan
Universitas Mikroskil
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Social Sciences

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Pelatihan Power Point Kreatif berbasis Animasi Pada Guru-guru SMP Swasta Wiyata Dharma Kristian Telaumbanua; Florida Damanik; Heru Kurniawan
Prosiding SISFOTEK Vol 5 No 1 (2021): SISFOTEK V 2021
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (491.64 KB)

Abstract

This training aims to provide teachers with an understanding of the usefulness of the various features found in the Microsoft PowerPoint application and how to use them to create a more interesting learning environment by using various animations during presentations/teaching. The animation that appears in a presentation is expected to make the presenter/teacher still get the attention of the listeners/students and students and reduce the level of boredom that arises due to the presentation slide containing too much text. The training provided has a level of complexity and difficulty that is adjusted to the current conditions of understanding of the teachers through a short survey conducted at the beginning of the training where teachers will be asked to provide an overview of knowledge about Microsoft PowerPoint in a form. After that the training team will carry out training exercises from the training that will be given to the teachers and separate several training sessions to follow the progress of the results of the teachers. The results of the training can be seen directly from the results of simple animations made by the teachers independently according to the instructions of the trainers. The results obtained during the training can later be used in making teaching and learning process modules or even presentation slides for non-academic needs such as introducing products, elaborating structures, showing a simple understanding of a process and so on.
Analisis Sentimen Kebijakan MBKM Berdasarkan Opini Masyarakat di Twitter Menggunakan LSTM Sio Jurnalis Pipin; Heru Kurniawan
Jurnal SIFO Mikroskil Vol 23, No 2 (2022): JSM VOLUME 23 NOMOR 2 TAHUN 2022
Publisher : Fakultas Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55601/jsm.v23i2.900

Abstract

Merdeka Belajar Kampus Merdeka (MBKM) merupakan kebijakan dari Kemendikbud RI yang berperan penting dalam pembelajaran yang otonom dan fleksibel pada kegiatan belajar mahasiswa diluar program studi. Namun, MBKM memiliki pro dan kontra sehingga perlu dilakukan analisis dan evaluasi kebijakannya untuk meningkatkan kinerja melalui umpan balik dari masyarakat. Penelitian ini akan melakukan sentimen analisis pada kebijakan MBKM pada tweet pengguna Twitter dari tahun 2020 - 2022 dengan kata kunci "MBKM", "MSIB" dan "merdeka belajar".Long-Short Term Memory (LSTM) digunakan untuk menganalisa sentimen multiclass pada tweet Bahasa Indonesia ke dalam 6 (enam) kelas emosi. Pengumpulan dan persiapan dataset dimulai dengan seleksi fitur, menghilangkan duplikasi dan seleksi tweet, kemudian dilakukan pre-processing yaitu case folding, tokenizing, pembersihan karakter, normalisasi hingga stemming untuk digunakan dalam pembobotan TF-IDF yang diperlukan dalam pembuatan model LSTM.Hasil penelitian ini menghasilkan model LSTM yang telah dilatih dari dataset 658 tweet dengan nilai akurasi terbaik di 80,42%. Analisis sentimen program MBKM dari tweet pengguna didominasi oleh perasaan "bingung" yaitu 39,51%, kemudian disusul oleh perasaan "senang" yaitu 16,26%, perasaan "sedih" yaitu 15,80%, perasaan "marah" yaitu 13,98%, perasaan "takut" yaitu 7,29%, dan perasaan "terkejut" yaitu 7,14%. Sehingga penting pengkajian untuk meningkatkan program MBKM agar memiliki prosedur dan pelaksanaan yang jelas sehingga mahasiswa nyaman dan memiliki sentimen positif terhadap program MBKM.
Pelatihan Instalasi Jaringan Komputer Menggunakan Simulasi Cisco pada SMK Methodist Tanjung Morawa Frans Mikael Sinaga; Sio Jurnalis Pipin; Heru Kurniawan
Journal of Social Responsibility Projects by Higher Education Forum Vol 4 No 1 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jrespro.v4i1.3633

Abstract

SMK Swasta Methodist Tanjung Morawa is one of the private schools under the auspices of Yayasan Methodist Kasih Imanuel Indonesia, which was established in 2008. Tanjung Morawa Methodist Private Vocational School has various majors, one of which is Network and Computer Engineering (TKJ). Network installation is one of the most interesting subjects to discuss because the students have studied it before and it is already a lesson that is in accordance with the majors of the students of Tanjung Morawa Methodist Private Vocational School, namely Computer Network Engineering (TKJ). The students have learned several computer network simulation applications such as virtual boxes but the network simulation applications studied are still limited, therefore, the Faculty of Informatics Universitas Mikroskil offers activities in the form of computer network installation training using Cisco simulation to improve the ability of students to have better competencies. This training activity lasted for 2 days and was carried out in the computer laboratory of Universitas Mikroskil. During this training activity the students were given pre-test questions, materials and case studies, post-test and final feedback.
Pengembangan Sistem Informasi Berbasis Web Pada Sekolah SMAN 12 Medan Dengan Metode Extreme Programming David Josua Augusto Tampubolon; Fiqri Ardiansyah; Rifan Habib Makarim; Syanti Irviantina; Heru Kurniawan
Jurnal SIFO Mikroskil Vol 24, No 2 (2023): JSM VOLUME 24 NOMOR 2 TAHUN 2023
Publisher : Fakultas Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55601/jsm.v24i2.1029

Abstract

Saat ini SMAN 12 Medan telah memiliki sebuah website dan terdapat portal untuk siswa dan guru, tetapi siswa dan guru tidak dapat masuk karena pengembang website tidak kunjung menyelesaikan fitur tersebut. Tujuan dari penelitian ini adalah untuk mengembangkan sebuah sistem informasi berbasis website untuk mempermudah kegiatan administrasi dan akademik sekolah SMAN 12 Medan. Metode yang tepat digunakan dalam penelitian ini adalah metode extreme programming, karena metode tersebut tepat digunakan ketika dibutuhkan perubahan yang cepat dan memiliki tim programmer yang sedikit. Hasil dari website yang telah dikembangkan ini memiliki fitur-fitur yang mampu mendukung kegiatan akademik sekolah. Di dalam website yang baru, pihak staf, guru, dan siswa dapat masuk melalui akun masing-masing. Selain itu pihak orangtua dapat memantau aktivitas siswa terkait. Dari hasil penelitian disimpulkan bahwa website tersebut telah berjalan sesuai dengan pengujian dari black box testing.
Prediksi Saham Menggunakan Recurrent Neural Network (RNN-LSTM) dengan Optimasi Adaptive Moment Estimation Sio Jurnalis Pipin; Ronsen Purba; Heru Kurniawan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i4.4014

Abstract

Predicting stock price movements is a complex challenge in the financial market due to unpredictable price fluctuations and high sensitivity levels. Noise in historical stock price data and temporal dependencies between previous and current prices make recognizing price movement patterns difficult. In a dynamic market environment, the model's ability to generate accurate predictions holds significant implications for more informed investment decision-making. The Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM) model holds great potential for stock price prediction. It captures temporal dependencies, identifies non-linear relationships, and deciphers complex trends in stock price data. This study employs deep learning techniques with the RNN-LSTM model optimized using Adaptive Moment Estimation (Adam) to enhance stock price prediction accuracy by leveraging historical stock price data and technical factors. Data preprocessing, including handling missing values and data normalization, aids the model in navigating the dataset's intricacies. Test results utilizing the Mean Squared Error (MSE) metric reveal the model's ability to produce predictions that closely resemble actual stock prices, with a low loss value of 0109012. The model also exhibits good predictive accuracy, as evidenced by a favorable Mean Percentage Error (MPE) score of 1.74% between predicted and actual values. These findings hold valuable implications for assisting investors and financial practitioners in managing complexity and uncertainty within the stock market
Object Detection in E-Commerce Using YOLO in Real Time Frans Mikael Sinaga; Gunawan; Sunaryo Winardi; Heru Kurniawan; Wulan Sri Lestari; Karina Mannita Tarigan
Teknika Vol 13 No 1 (2024): Maret 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i1.773

Abstract

Presently, e-commerce platforms incorporate image search functionalities. Nevertheless, these systems possess constraints; input images necessitate static and manual cropping since the system does not automatically generate bounding boxes. Addressing this concern requires the implementation of an object detection algorithm to ascertain the quantity, location, and type of desired objects within real-time bounding boxes before users finalize their selection. This capability empowers users to readily discern their desired items, thereby augmenting the precision and efficiency of visual searches. Despite the availability of swifter object detection algorithms such as R-CNN and Mask R-CNN, which prioritize accuracy over speed, rendering them less suited for real-time detection, we opted to employ the YOLOv4 algorithm as an alternative, renowned for its efficacy in real-time object detection. Furthermore, we adopted the Color, Texture, and Edge-Based Image Retrieval (CTEBIR) technique for image matching. The results of our experimentation demonstrate that the utilization of the YOLOv4 algorithm can enhance the accuracy and speed of visual searches by streamlining the search process based on the identified classes. Additionally, our precision assessment yielded a score of 95%, with individual scores for camera objects reaching 90%, keyboards achieving 85%, and laptops attaining 71%. These findings corroborate the dependability of the CTEBIR algorithm in image matching and contribute to a deeper comprehension of the system's efficacy in accurately detecting and distinguishing objects.
Co-Authors David Josua Augusto Tampubolon Fiqri Ardiansyah Florida Damanik Frans Mikael Sinaga Gunawan Gunawan Karina Mannita Tarigan Kristian Telaumbanua Rifan Habib Makarim Ronsen Purba Sinaga, Frans Mikael Sio Jurnalis Pipin Sio Jurnalis Pipin Sunaryo Winardi Syanti Irviantina Wulan Sri Lestari