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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Information Systems Engineering and Business Intelligence Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Teknoinfo IJISTECH (International Journal Of Information System & Technology) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurnal Informatika Kaputama (JIK) Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Journal of Computer Networks, Architecture and High Performance Computing IJISTECH Yayasan Cita Cendikiawan Al Khwarizmi INCODING: Journal of Informatics and Computer Science Engineering Brilliance: Research of Artificial Intelligence Sisfo: Jurnal Ilmiah Sistem Informasi Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Jurnal Solusi Masyarakat Dikara
Nurul Khairina
Universitas Medan Area
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Analysis of The Multilayer Perceptron Algorithm on Twitter User’s Sentiment Towards The COVID-19 Vaccine Fordinand Halomoan Pasaribu; Nurul Khairina; Dian Noviandri; Susilawati Susilawati; Rahmad Syah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.9664

Abstract

The World Health Organization (WHO) declared COVID-19 a global pandemic due to its rapid spread and infection of people worldwide. The emergence of COVID-19 vaccines has garnered both support and rejection from the public. Some people support the vaccines, while others remain cautious, even though the government provides them for free. The procurement of coronavirus vaccines has generated diverse opinions in society. COVID-19 vaccines have become a trending topic on social media, particularly on Twitter. This research aims to explore public opinions on the COVID-19 vaccine. The methods used in this study include data collection, text preprocessing, TF-IDF, multilayer perceptron algorithm, and testing with confusion matrices. Out of a total of 228,208 positive, negative, and neutral opinions from Twitter users about the COVID-19 vaccine, with a training-to-testing ratio of 90% to 100%, the model will learn more by using a large amount of training data. The performance results of this research obtained the highest accuracy of 81.2%, precision of 83.8%, and recall of 71.2%. The results of sentiment analysis can be seen in the public opinions on the COVID-19 vaccine, which are divided into three categories: 35% positive opinions, 16.3% negative opinions, and 48.7% neutral opinions. The word cloud results show that positive opinions revolve around three topics: availability, cost, and dosage. Negative opinions from Twitter users about the COVID-19 vaccine focus on two main issues: vaccine side effects and deaths. Neutral opinions cover three topics, including dosage, availability, age, and expiration date
Analisis Perbandingan Metode SMART Dan MFEP Dalam Menentukan Driver Bus Terunggul Pada PT. Putra Pelangi Perkasa Muhammad Fikri Riandra; Andre Hasudungan Lubis; Nurul Khairina
Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Vol 1, No 2 (2022): Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Publisher : Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jitek.v1i2.1471

Abstract

Human Resources (HR) in a transportation companies may affect many aspects that determine work success, especially the drivers. Competition among drivers is getting tougher, hence the companies face difficulties in finding the best bus drivers. Nevertheless, the assessment still uses the traditional method, namely by using a book as the calculation. The study used the Simple Multi-Attribute Rating Technique (SMART) and Multi Factor Evaluation Process (MFEP) methods to rank drivers’ performance in terms of discipline, knowledge, loyalty, and years of service. The system used in this study also compares the two methods with the actual rank from the company. The population in this study was 90 respondent, with a sample data of 10 drivers. The results show that the SMART method is closer to manual ranking, which has 7 similarities out of 10 ranking sequences or 70% accuracy rate with the manual calculations. On the other hand, only 4 similarities out of 10 ranking se-quences were generated by the MFEP method. Thus, the MFEP method only has a 40% accuracy rate with manual calculations. To sum up, the research created a decision support system that has certainty in choosing the best bus driver to assists the leaders to find the best driver to get an exclusive bus.
KLASIFIKASI DAUN TEH SIAP PANEN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK ARSITEKTUR MOBILENETV2 Marpaung, Febriady; Khairina, Nurul; Muliono, Rizki; Muhathir, Muhathir; Susilawati, Susilawati
Jurnal Teknoinfo Vol 18, No 1 (2024): Januari
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v18i1.3435

Abstract

Penentuan waktu panen daun teh adalah faktor penting dalam industri teh yang secara signifikan mempengaruhi kualitas dan nilai jual produk. Maka dari itu, para petani teh dan produsen perlu memahami waktu yang tepat untuk memetik daun teh untuk menghasilkan teh berkualitas tinggi. Klasifikasi daun teh siap panen dapat menjadi solusi efektif untuk membantu dalam menentukan waktu panen yang optimal. Dalam rangka mencapai tujuan ini, pendekatan digital menjadi semakin penting, di mana pengenalan otomatis daun teh dapat dilakukan dengan cepat dan akurat. Salah satu metode yang digunakan dalam penelitian ini adalah Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2. Convolutional Neural Network (CNN), khusus dirancang untuk mengolah data dua dimensi seperti gambar. Keunggulan CNN terletak pada kemampuannya memahami dan mengklasifikasi aspek dan objek dalam citra. Salah satu arsitektur model Deep Learning menggunakan CNN yang populer adalah MobileNet. MobileNetV2, sebagai modifikasi MobileNet, memperkenalkan inverted residual blocks dan linear bottleneck. Perbedaan utama dengan MobileNetV1 terletak pada penggunaan bottleneck, yang memungkinkan model mengubah input dari tingkat rendah ke deskriptor tingkat tinggi. MobileNetV2 mampu ekstraksi fitur otomatis dan efisien melalui inverted residual blocks, dan linear bottleneck meningkatkan kapabilitas model dalam mengolah informasi. Dengan pendekatan ini, MobileNetV2 menggunakan CNN sebagai alat efektif untuk tugas-tugas klasifikasi citra, menawarkan kemampuan ekstraksi fitur otomatis dan pengolahan informasi yang efisien dalam pengembangan Deep Learning. CNN telah terbukti efektif dalam tugas klasifikasi gambar, dan MobileNetV2 dikenal karena ringan dan efisien dalam penggunaan sumber daya. Dengan menggunakan metode ini, penelitian ini bertujuan untuk mengklasifikasikan tingkat kematangan daun teh. Hasil training dari enam skenario model yang diuji menunjukkan bahwa tingkat akurasi tertinggi tercapai pada pengujian skenario model 2, yaitu sebesar 100%. Pada pengujian ini, hyperparameter yang optimal termasuk epoch sebanyak 50, input shape RGB Channel sebesar 224x224x3, batch size sejumlah 32, dan optimizer yang digunakan adalah Adam. Pentingnya akurasi terlihat dari hasil pengujian menggunakan data testing, di mana model berhasil mencapai akurasi sebesar 100%. Selain itu, nilai presisi, recall, dan f1-score juga mencapai 100%. Hal ini menunjukkan bahwa model yang dikembangkan mampu mengenali dan mengklasifikasikan daun teh siap panen dengan sangat baik
The Impact of k-means on Association Rules Mining Algorithms Performance Hasudungan, Andre; Muliono, Rizki; Khairina, Nurul; Novita, Nanda
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20907

Abstract

Association Rule Mining (ARM) is one of unsupervised learning approach of machine learning. It acts as a data analysis technique that enables the identification of frequent patterns, correlations, associations, and causal structures within certain datasets. This method widely used in numerous studies and practices to explore knowledges and strengthen decision making. However, dealing a large dataset with high number of transactions may become the shortcoming for the ARM algorithms, such as Apriori, FP-Growth, and Eclat. It leads them to face several challenges, including computational complexity, long mining durations, and memory consumption. Hence, this paper proposes k-means clustering to generates several groups of data to handle the issue, then proceed the ARM algorithms for each individual produced cluster. The study used Elbow method and Silhouette Coefficient as the method to determining optimum number of clusters to be used. The result pointed out that k-means-ARM generates a greater number of rules and provides more contextually relevant and significant correlations. In term of Lift Ratio average score, the k-means-ARM shows the greater value rather than non k-means ARM. The k-means-ARM combination is robust; this approach improves the efficiency and scalability of ARM for large datasets and enhances the interpretability of the discovered association rules
Sistem Informasi Manajemen Data Surat Dengan Algoritma Blowfish Sitopu, Essay Puspita; Khairina, Nurul; Muliono, Rizki; Muhathir, Muhathir
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 6 No. 1 (2022): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2022
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v6i1.7964

Abstract

Perkembangan teknologi informasi yang semakin pesat saat ini menuntut kita untuk mampu mengikuti perkembangannya. Komunikasi secara digital di era modern dapat menjadi salah satu ancaman akan adanya penyadapan, penyalahgunaan informasi, dan pencurian data penting yang tidak diinginkan oleh pihak yang tidak berwenang. Apalagi jika informasi tersebut bersifat rahasia dan memliki ruang lingkup yang luas dalam kenegaraan. Oleh karena itu, keamanan penyimpanan data menjadi sangat penting. Data yang terdapat pada surat masuk ataupun surat keluar dalam satu sekolah juga memliki kerahasiaan yang harus dijaga, kemana surat dikirimkan dan dari mana surat berasal juga menjadi salah satu hal yang perlu dijaga kerahasiannya. Atas dasar inilah peneliti ingin membangun sistem informasi manajemen surat yang berfokus pada penyimpanan data surat atau database surat. Dari berbagai metode penyandian yang ada hingga saat ini, salah satunya adalah metode Kriptografi Blowfish yang menggunakan blok cipher 64-bit dengan panjang kunci variabel. Proses enkripsi pada penelitian ini merupakan proses memberikan kunci rahasia pada surat tersebut agar surat tersebut berubah menjadi tulisan sandi yang tidak dapat dipahami oleh pihak yang tidak berwenang. Proses deskripsi merupakan proses menterjemahkan kembali surat yang telah terahasia agar pihak sekolah dapat membaca surat yang dimaksud. Dari hasil pengujian sistem informasi, sistem informasi manajemen surat dapat menerapkan Algoritma Blowfish dengan baik, hal ini ditandai dengan adanya perubahan nomor surat, tanggal pengiriman surat serta tujuan pengiriman surat pada saat proses enkripsi maupun pada saat proses dekripsi.
Perancangan Sistem Informasi Inventori Gudang Pada PT.Del Citra Mandiri Siagian, Yorris; Khairina, Nurul
Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Vol 4, No 1 (2025): Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jitek.v4i1.5669

Abstract

The development of information technology has encouraged PT. DEL Citra Mandiri, a radio broadcasting company, to adapt to the management of warehouse inventory that is still done manually using Microsoft Excel, is at risk of errors and data loss. This research aims to design a web-based inventory information system to improve the accuracy and efficiency of inbound and outbound goods management. This research uses a system development method based on a structured approach that includes analysis, design, implementation, and testing. System design is carried out with tools such as Flowchart, DFD, Use Case Diagram, and ERD. The technologies used include XAMPP as a local server, MySQL for databases, and Figma for user interface design. The results of the study show that the designed inventory information system is able to overcome the obstacles of manual recording, improve data accuracy, and facilitate real-time monitoring of stock of goods. With the implementation of this system, PT. DEL Citra Mandiri is expected to improve operational efficiency and support more accurate decision-making in warehouse management.
DECISION SUPPORT SYSTEM IMPLEMENTATION IN DETERMINING STUDENTS TO RECEIVE BOS FUNDING USING THE WASPAS METHOD Napisah, Napisah; Muliono, Rizki; Khairina, Nurul; -, Muhathir
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4046

Abstract

Success in learning and learning activities at SMA Asy-Syafiiyah Medan, is not only influenced by teachers, but also by student aspects such as attendance, parental income, activity participation, achievement scores, and discipline. To obtain optimal results, the authors designed an application using the Weighted Aggregated Sum Product Assessment (WASPAS) method that can determine students who receive BOS funds. After calculating 5 times with predetermined criteria, Rizki Ridho Silalahi's final result was 0.9197. The system designed for receiving BOS Fund assistance at SMA Asy-Syafiiyah Medan has been tested by inputting criteria data and carrying out the calculation process using the WASPAS method.
Penerapan Mobilenetv3 untuk Klasifikasi Jenis Bahan Pakaian Sinaga, Doni Poulus; Khairina, Nurul
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.829

Abstract

This study aims to develop an efficient and accurate model for classifying clothing material types using the MobileNetV3 architecture. Clothing material images were collected from open sources and processed through resizing, normalization, and data augmentation. The model was trained using transfer learning and evaluated using accuracy, precision, recall, and F1-score metrics. The evaluation results showed an accuracy of 92%, with the best performance in the silk and polyester categories. However, misclassifications still occurred for materials with similar textures, such as linen and cotton. Compared to previous studies, this approach offers advantages in computational efficiency for mobile and edge computing applications. This research contributes to the development of an automated clothing material classification system to support the textile and fashion industries. Further improvements are needed by enhancing dataset quality and fine-tuning the model to better distinguish materials with visually similar characteristics.
KLASIFIKASI KESEHATAN JANIN PADA IBU HAMIL MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Darkani, M. Farhan; Khairina, Nurul
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.830

Abstract

Monitoring fetal health is a crucial aspect of pregnancy, requiring accurate and efficient methods for early detection of potential complications. This study aims to develop a fetal health classification system using the Support Vector Machine (SVM) algorithm. The data analyzed includes various fetal physiological parameters obtained through routine examinations, such as heart rate, fetal movements, and other relevant indicators. SVM was chosen due to its capability to handle non-linear data and its high classification accuracy. The classification process involves data preprocessing, feature selection, model training, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. The results indicate that SVM can effectively classify fetal health conditions with high accuracy, making it a promising diagnostic support tool for medical professionals. This study contributes to maternal and fetal healthcare by offering a machine learning-based approach that enhances the effectiveness of fetal health monitoring systems.  
Pelatihan Peningkatan Kemampuan Penterjemahan Artikel Ilmiah dengan Grammarly, Google Translate dan Paraphrasing Khairina, Nurul; Albra, Wahyuddin
Jurnal Solusi Masyarakat Dikara Vol 1, No 1 (2021): Desember 2021
Publisher : Yayasan Lembaga Riset dan Inovasi Dikara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Konsep webinar mulai marak diminati sejak masa pandemic Covid-19, namun sampai tahun ini pun para akademisi masing sering melakukan webinar dengan berbagai tema acara. Pelatihan dengan konsep webinar tidak mengurangi tingkat pemahaman peserta webinar, bahkan dengan adanya webinar, narasumber dan peserta yang berbeda kota dan berbeda negara pun dapat lebih leluasa berbagi ilmu. Kegiatan secara daring bukan hanya dilakukan untuk webinar saja, namun juga dapat digunakan untuk aktifitas akademi dosen seperti mengajar, penelitian, pengabdian dan juga penunjang. Salah satu kegiatan pengabdian dalam bentuk webinar juga sering dilakukan oleh para akademisi. Pelatihan yang dilakukan oleh penulis berikut ini merupakan pelatihan yang dilakukan melalui webinar yang dilaksanakan oleh ITScience, dimana penulis merupakan salah satu narasumber pada webinar tersebut. Pelatihan yang dibawakan oleh penulis merupakan pelatihan peningkatan kemampuan menterjemahkan artikel ilmiah dengan tools Grammarly, Google Translate dan Paraphrasing. Dalam menterjemahkan kalimat, peserta diarahkan untuk menterjemahkan lebih dari satu kali tahapan, sebainya peserta menterjemahkan sebanyak tiga kali tahapan. Dalam menggunakan Google Translate, peserta juga diajarkan menterjemahkan kalimat dengan fitur Google Transalate yang diiringi dengan tools Grammarly untuk memperbaiki tenses, kata benda, kata sifat dan kata keterangan. Tahapan terjemahan yang terakhir adalah memperbaiki kesesuaian parafrase yang dapat dilakukan dengan menggunakan tools Paraphrasing. Kegiatan pengabdian yang dilakukan melalui webinar ini berjalan dengan baik dan antusias peserta dapat dilihat dari kehadiran peserta yang mencapai ± 90 orang.
Co-Authors -, Muhathir Adidtya Perdana, Adidtya Ahmad Faisal Andre Hasudungan Lubis Clawdia, Jhessica Darkani, M. Farhan Dian Noviandri Fordinand Halomoan Pasaribu Hasudungan, Andre Juanda Hakim Lubis Juanda Hakim Lubis Juanda Hakim Lubis Marpaung, Febriady Mhd Zulfansyuri Siambaton Muhammad Fikri Riandra Muhammad Khoiruddin Harahap Muhathir, Muhathir Muhathir, Muhathir Nanda Novita, Nanda Napisah, Napisah Rahmad B.Y Syah Rizki Muliono Robin Robin Siagian, Yorris Sinaga, Doni Poulus Siregar, Saut Dohot Sitopu, Essay Puspita Susilawati Susilawati Susilawati, Susilawati Theofil Tri Saputra Sibarani Wahyuddin Albra, Wahyuddin Zulfikar Sembiring