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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Journal of Computer Networks, Architecture and High Performance Computing Yayasan Cita Cendikiawan Al Khwarizmi Data Sciences Indonesia (DSI)
Mawaddah Harahap, Mawaddah
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Deblurring Citra Dengan Metode Lucy Richardson Deconvolution William, William; Harahap, Mawaddah; Sihombing, Josua Parulian; Lubis, Abdul Rahman
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 3 No. 1 (2020): Jutikomp Volume 3 Nomor 1 April 2020
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v3i1.1387

Abstract

Adanya file gambar hasil scan tidak memiliki kualitas yang bagus(blur). Citra blur ini menyebabkan objek yang terdapat didalamnya menjadi tidak terlihat jelas. Tujuan dari penelitian ini adalah untuk meningkatkan kualitas citra pada dokumen dari hasil scan dengan melakukan deblurring dengan menggunakan algoritma Lucy Richardson Deconvolution. Teknik yang digunakan untuk menghilangkan kekaburan citra (deblurring), penapisan dengan merestorasi citra dengan menggunakan metode iteratif, yaitu algoritma Lucy-Richardson. Metode Lucy-Richardson dapat melakukan deblurring pada citra dengan nilai threshold 192. Maksimal blur yang bisa di deblurring adalah sampai dengan 50% blur pada citra menggunakan pengujian PSNR dengan nilai mininum 30dB. Penerapan metode Lucy-Richardson menggunakan 1 parameter (Threshold), sehingga lebih memudahkan untuk mendapatkan hasil citra yang paling optimal
Prediksi Employee Churn Dengan Uplift Modeling Menggunakan Algoritma Logistic Regression Kinoto, Jovan; Damanik, Jansen Liharma; Situmorang, Erwin Tri Saputra; Siregar, Josua; Harahap, Mawaddah
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 3 No. 2 (2020): Jutikomp Volume 3 Nomor 2 Oktober 2020
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v3i2.1645

Abstract

Pada sebuah perusahaan, karyawan merupakan aset yang berharga dan dapat menunjang kesuksesan perusahaan tersebut. Namun, hilangnya tenaga kerja dapat merugikan perusahaan. Kondisi ini disebut dengan Employee Churn. Salah satu solusi untuk mengatasi Employee Churn adalah dengan menerapkan model Uplift Modeling. Dalam penelitian ini, penulis menganalisa penerapan Logistic Regression terhadap Uplift Modeling dalam permasalahan Employee Churn. Data yang diteliti adalah data karyawan dari IBM HR Analytics. Hasil prediksi pada penelitian ini mendapat akurasi sebesar 64,40%, sedangkan hasil preskripsi menghasilkan hasil yang cukup baik apabila menerapkan waktu kerja tambahan pada karyawan. Berdasarkan hasil yang didapat, diketahui bahwa para karyawan justru cenderung bertahan di perusahaan apabila diberikan waktu kerja tambahan.
Penggunaan Recurrent Neural Network Dalam Mendeteksi Sentimen Berbahaya Pada Platform Media Sosial Chuanta, Roy Vidia; Harahap, Mawaddah; Putra, Adya Zizwan
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 7 No. 1 (2024): Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v7i1.4845

Abstract

Social media platforms in today's modern era make it easier for people to communicate and socialize. Behind that, on a hot topic, there must be sentiment. Every sentiment the community conveys varies; some are good and neutral, and some are bad or dangerous. To detect sentiment, start from crawling data, pre-processing, labeling, and then testing or training to get accuracy value, recall value, f1 value, and precision value using Long Short Term Memory. They obtained an accuracy value of 0.582, recall value of 0.582, f1 value of 0.428, and precision of 0.339. This LSTM model can be used to develop an analysis model that is successfully achieved.
Edge Detection Of Potato Leaf Damage With Laplacian Of Gaussian Algorithm Harahap, Mawaddah; Wijaya, Adrian Christian; Pasaribu, Samuel Henock Hasangapon; Sembiring, Giovan; Ginting, Kenjiro Christian
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11583

Abstract

The Potato plants are type young plant that easily attacked by pests and diseases, part of plant that often attacked by disease is leaves which can affect growth process and reduce crop yields. One way to determine if potato leaf is healthy or unhealthy is by using the edge detection method. Crop failure in potato plants can be detected through damage to leaves. The purpose of this study was to help facilitate identification type of damage to leaf margins of potato plants by applying the Laplacian of Gaussian algorithm. Based on results of testing on several research datasets sourced from the Agricultural Sector of the Karo Regency Government through an application of edge image detection on potato plant leaves through a grayscale, threshold and detection process with the Laplacian of Gaussian algorithm. It takes the longest time of 12.34 s with an error of 1.45 on the type of damage caused by aphids and at least 6.03 s with an error of 0.71 on the normal leaf edge detection results. Based on test results on 17 potato leaf images, the average test time is 8.45 s
Classification of Tuberculosis Based on Lung X-Ray Image With Data Science Approach Using Convolutional Neural Network Harahap, Mawaddah; Pasaribu, Alfeus P. S.; Sinaga, Dedy Ridoly; Sipangkar, Romulus; Samuel, Samuel
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i4.11711

Abstract

Tuberculosis (TB) is a potentially serious infectious disease in the lungs, becoming 1 of 10 causes of death. In Indonesia, the disease is ranked third after India and China with 824,000 cases and 93,000 deaths per year, equivalent to 11 deaths per hour. The increasing number of infections and deaths from TB disease is recorded as a result of its transmission, lack of early diagnosis, and inadequate professional radiologists in developing areas where TB is more common. Rapid and accurate diagnosis is essential for appropriate treatment to be initiated. Diagnosis is usually done by looking at the results of the x-ray image of the thorax and the results of the BTA test on the patient. To classify lung x-ray images detected tuberculosis or not, a study was carried out using the Convolutional Neural Network (CNN) method. The test results produce the last epochs value of 200, the accuracy obtained is 0.9892, which means the CNN accuracy is 98%, with validation the accuracy obtained is 0.9835 or 98%. So the results of the classification test using CNN are quite accurate. With the acquisition of CNN results which is quite high, it can be used as a consideration to be used in classifying TB disease.
Fake Article Detection Application With Rabin Karp Algorithm Harahap, Mawaddah; Marcel, Rico; Milatrisna, Dwi Yunita; Kuswulandari, Sri
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11949

Abstract

Currently, fake (fake) information/articles are increasingly widespread. Surveys show that people receive fakes more than once a day. The most used channel for spreading fakes is social media. The fake phenomenon in Indonesia raises doubts about the information received and confuses the public. Artificial intelligence is an intelligent machine (computer), where a machine is trained in advance (machine learning) to solve certain problems. This artificial intelligence is expected to work faster and more accurately. There are several methods that can be implemented, one of which is using the Rabin-Karp algorithm. Where the reason for choosing the Rabin karp algorithm is that there is a process that can filter existing writing so that it is suitable for the problems discussed, namely article writing and an algorithm that can work at high speed so that users do not need a long time to receive predictive results for articles.
Coffee Quality Prediction with Light Gradient Boosting Machine Algorithm Through Data Science Approach Putra, Adya Zizwan; Harahap, Mawaddah; Achmad Nurhadi; Andro Eriel Tambun; Syahmir Defha
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.12169

Abstract

In increasing sales by increasing consumer satisfaction with the quality of coffee sold. A way is needed to make it easier to predict the determination of quality coffee so as to increase the efficiency of the coffee sorting process which does not take a long time and can increase the productivity of companies that have competitiveness. Several developments have been made to improve the performance of the algorithm which has the potential to produce good quality predictions. Import Copy Data into a format that can be processed to a later stage or with a Machine Learning algorithm. Copy data that can be processed is then modified in such a way as to ensure that the data is suitable for use in Data Science or Machine Learning processes. By using coffee data specifications from the plantation to the coffee beans produced, it is expected that coffee quality can be predicted quickly without the need for manual calculations or analysis by humans. The working procedures for selecting the quality of coffee beans are coffee import data, coffee data processing, split test-train coffee data, light gradient enhancement machine, yield prediction, and Performance Prediction Evaluation. The amount of data used is 1,339 data. The dependent variable in this data is Coffee Quality while the rest will be cleaned and processed to serve as an independent variable. The accuracy rate of the algorithm in predicting coffee quality is 72%.
Teacher Quality Affects On Graduation Of Study Programming Data Approach There With CRISP-DM Method Harahap, Mawaddah; Hidayati, Namira; Panjaitan, Sumiati; Tambunan, Enjelyna; Sihombing, Juniati
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12762

Abstract

Each student's graduation is influential to the teacher in every subject that can be predicted based on the pattern of habits of the teacher who presents the subject. Web Proggramming is the subject of study that must be completed by every student. If this course is not completed, it is not allowed for the student to take other courses related to it. The custom patterns of teachers in this study were taken from 300 student respondents. An analysis is done to compare the results of questionnaire scores with the assessment of college admissions teachers. From the results of the comparison, it is possible to predict the graduation rate of students to the web programming course. The results of the experiment were that 72% of the students received highly influential predictions, 12% Influential, 7% Sufficient, 5% Influential and 4% Highly Influential.
Skin cancer classification using EfficientNet architecture Harahap, Mawaddah; Husein, Amir Mahmud; Kwok, Shane Christian; Wizley, Vincent; Leonardi, Jocelyn; Ong, Derrick Kenji; Ginting, Deskianta; Silitonga, Benny Art
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7159

Abstract

Skin cancer is one of the most common deadly diseases worldwide. Hence, skin cancer classification is becoming increasingly important because treatment in the early stages of skin cancer is much more effective and efficient. This study focuses on the classification of three common types of skin cancer, namely basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma using EfficientNet architecture. The dataset is preprocessed and each image in the dataset is resized to 256×256 pixels prior to incorporation in later stages. We then train all types of EfficientNet starting from EfficientNet-B0 to EfficientNet-B7 and compare their performances. Based on the test results, all trained EfficientNet models are capable of producing good accuracy, precision, recall, and F1-score in skin cancer classification. Particularly, our designed EfficientNet-B4 model achieves 79.69% accuracy, 81.67% precision, 76.56% recall, and 79.03% F1-score as the highest among others. These results confirm that EfficientNet architecture can be utilized to classify skin cancer properly.
Pengembangan aplikasi Multi-Platform dan Back-End Sesuai Dengan Standar Industri Simarmata, Allwin; Putra, Adya; Husein, Amir; Harahap, Mawaddah
Dedikasi Sains dan Teknologi (DST) Vol. 4 No. 1 (2024): Artikel Periode Mei 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v4i1.4015

Abstract

Di era digital yang terus berkembang pesat, kemampuan beradaptasi dengan perubahan teknologi menjadi kunci kesuksesan bagi para pegawai. Untuk itu, kegiatan pengabdian masyarakat ini dirancang dengan tujuan meningkatkan wawasan dan pengetahuan pegawai dalam menghadapi disrupsi digital. Kegiatan ini, yang berlangsung pada 15 Maret 2024 di Menara Mandiri Medan, berfokus pada pelatihan pengembangan aplikasi multi-platform dan back-end. Pelatihan ini memberikan pemahaman mendalam tentang teknologi terbaru serta keterampilan praktis yang diperlukan dalam mengembangkan aplikasi yang efisien dan mudah diakses di berbagai platform, seperti iOS, Android, dan Windows. Dalam pelatihan ini, peserta juga mempelajari pentingnya infrastruktur back-end yang kuat dan aman untuk meningkatkan skalabilitas, keamanan, dan kinerja aplikasi secara keseluruhan. Mereka diajarkan tentang bahasa pemrograman, basis data, dan layanan cloud yang mendukung operasi back-end. Selain itu, pelatihan ini menekankan pentingnya mengikuti pedoman dan standar industri untuk memastikan aplikasi yang dikembangkan memenuhi standar keamanan, kinerja, dan kualitas. Hasil dari kegiatan ini menunjukkan peningkatan kompetensi digital pegawai, mempersiapkan mereka untuk menghadapi perubahan dan mengidentifikasi peluang serta tantangan yang muncul dari perubahan teknologi. Dengan pengetahuan yang diperoleh, pegawai lebih siap untuk mengembangkan aplikasi berkualitas tinggi yang sesuai dengan standar industri. Kesimpulannya, kegiatan pengabdian masyarakat ini berhasil meningkatkan wawasan dan pengetahuan pegawai dalam menghadapi disrupsi digital, yang sangat relevan dan penting dalam era digital yang semakin kompetitif.
Co-Authors Abdi Dharma Achmad Nurhadi Agung Prabowo Amir Husein Amir Mahmud Husein, Amir Mahmud Amir Saleh Andika, Ahmad Zaki Andro Eriel Tambun Angelina, Valencia Barti, Surya Batu Bara , Yacobus M.T. Celvin Chuanta, Roy Vidia Ciptady, Kalvintirta Damanik, Jansen Liharma Donpril, Meleyaki Duran, Filbert Evander, Oscar Fernandito, Peter Ginting, Deskianta Ginting, Kenjiro Christian Hadyanto, David Hendra Sihombing Hidayati, Namira Hulu, Yakin Rianto Husein , Amir Mahmud Hutagalung, Delano Ariesagita Ibadurrahman Ibadurrahman Indra, Evta Jefferson, Jefferson Jonvin, Jonvin Juliani, Fenny Kinoto, Jovan Kusuma, Leonardo Kuswulandari, Sri Kwok, Shane Christian Leonardi, Jocelyn Lubis, Abdul Rahman Malau, Johannes Rianto Marcel, Rico Milatrisna, Dwi Yunita Nababan, Siska Yanti Ndruru, Yonata Ong, Derrick Kenji Panjaitan, Sumiati Pasaribu, Alfeus P. S. Pasaribu, Samuel Henock Hasangapon Pratama, Ari Rizki Purba, Juniven Francisco Purba, Windania Putra, Adya Putra, Adya Zizwan Rozi, Fachrul Samosir, Suprianto Samuel Samuel Sembiring, Giovan Sihombing, Josua Parulian Sihombing, Juniati Silitonga, Benny Art Simangungsong, Tentus Natoka Simarmata, Allwin Sinaga, Dedy Ridoly Sipangkar, Romulus Siregar, Josua Siregar, Saut Dohot Siregar, Wali Siti Aisyah Situmeang, Candra Ebenezer Situmorang, Erwin Tri Saputra Suryani, Melva Syahmir Defha Tambunan, Enjelyna Turnip, Christi Andika waruwu , Februari Kurnia Wijaya, Adrian Christian Wijaya, Frederico William William Winata, Davin Wizley, Vincent Yennimar, Yennimar Yusniar Lubis Zakarias Situmorang Zizwan Putra, Adya