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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JURTEKSI Jurnal Armada Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) LEBAH Jurnal Pengabdian Pada Masyarakat METHABDI JURNAL VOKASI TEKNIK (JUVOTEK) Manajemen : Jurnal Ekonomi
Johanes Terang Kita Perangin Angin
STMIK TIME
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MEMBANGUN SENSE OF BELONGING (RASA MEMILIKI) INDIVIDU DAN MENERAPKANNYA SEBAGAI WUJUD MOTIVASI DIRI DALAM BEKERJA DAN KECINTAAN TERHADAP ORGANISASI PADA YPK DON BOSCO KAM Maria, Elvie; Sudarso, Andriasan; Perangin-Angin, Johanes Terang Kita
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 3 No 1 (2023): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol3No1.pp104-112

Abstract

Sense of belonging is a something valuable that is owned by individuals in the organization when working. An understanding of diversity is not enough to build a positive work environment. It takes a strong sense of belonging to the organization by individuals as organizational human resources, so that a sense of responsibility and genuine effort is created in each individual's performance. Someone who has a sense of belonging will be able to act caring, attached, have empathy, be motivated and even be able to empower himself even without any encouragement. The seminar activity aims to grow and develop the knowledge, attitudes and skills for YPK Don Bosco KAM principal candidates, so that they are able to properly manage schools entrusted by the institution to them and support the success of improving the quality of learning in schools, building good social relations, which makes the school a second home for students, teachers and educational staff, and most importantly realizes that building a sense of belonging starts with oneself, from small things and to started from now, and applying it as description of self-motivation at work and applying it as description of self-motivation in work and love to the organization.
Complete Kernel Fisher Discriminant (CKFD) and Color Difference Histogram for Palm Disease Perangin Angin, Johanes Terang Kita; Herman, Herman; Joni, Joni
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.v8i4.14145

Abstract

Palm oil plantations play a significant role in the economy of Indonesia, supporting 16.2 million people. However, plant diseases pose a major threat to the productivity and health of palm oil crops. Early detection of these diseases is essential to prevent yield losses and mitigate damage. This study proposes the application of the Complete Kernel Fisher Discriminant (CKFD) method combined with Color Difference Histogram to classify diseases affecting oil palm fronds and leaves. The CKFD method uses a non-linear kernel transformation to improve the performance of Fisher Linear Discriminant Analysis (FLDA), while the Color Difference Histogram enhances sensitivity to color variations in different lighting conditions. Experimental results demonstrate that the CKFD method achieves superior accuracy in disease detection compared to traditional Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). The proposed approach showed an average accuracy of 94.5% for detecting diseases like Curvularia sp and Cochliobolus carbonus. The combination of CKFD with Color Difference Histogram significantly reduces the impact of lighting variations on the classification results, making it a robust solution for practical deployment in palm oil plantations. This research provides an effective tool for early disease detection and management in the palm oil industry.
Implementation of Deep Learning Model for Classification of Household Trash Image Robet, Robet; Perangin Angin, Johanes Terang Kita; Pribadi, Octara
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.v8i4.14198

Abstract

The problem of household waste management is a very important issue today, where the rapid urbanization, consumptive culture, and the tendency to dispose of waste without sorting it first from home, makes the volume of waste in landfills increase. Therefore, household waste management needs to be managed quickly and appropriately, so as not to have a major impact on environmental, hygiene, and health problems. Although some environmental communities and local governments have made efforts to manage waste through recycling systems, the long-term use of human labor is inefficient, expensive, and harmful to workers' health. Therefore, utilizing artificial intelligence technology is the best solution to classify waste types quickly and accurately. This research tries to test several pre-trained convolutional neural network (CNN) models to perform classification. The results of testing pre-trained CNN models, such as AlexNet, VGG16, VGG19, ResNet50, and ResNeXt50, found that the pre-trained model ResNext50 is better with 100% accuracy, while the training loss and validation loss are 0.0414 and 0.0304, respectively. Then the second best model is the pre-trained ResNet50 model with 100% accuracy with training loss and validation loss of 0.0832 and 0.1077, respectively.
Improving Resnet Model In Safety Gear Classification Using Finest Optimizer Robet; Johanes Terang Kita Perangin Angin; Edi Wijaya
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.703

Abstract

The Occupational accidents that occur in the work environment are increasing day by day. This is caused by workers' non-compliance with the established work safety equipment. Although the supervision of the use of work safety equipment has been carried out, it is still done manually involving less effective human resources. Therefore, it is necessary to develop an intelligent model that can classify the use of work safety equipment more accurately. This study uses the pre-trained ResNet50 model and is combined with the best optimization model to improve accuracy. The results of the study showed that the RMSProp optimization model has better performance with an accuracy value of 97.01% in the 17th epoch of 50 epochs of data training and with training loss and validation loss values ​​of 0.3268 and 0.145, respectively. Testing of 20 images with each image, 10 images using safety equipment, and 10 images not using safety equipment can be classified correctly.
IMPLEMENTASI ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) DENGAN OPTIMIZER ADAM DALAM DETEKSI EMOSIONALPADA WAJAH MANUSIA Amalia, Poppy; Wijaya, Robby; Chandra, Chandra; Octaviandy, Pieter; Wilson, Wilson; David, David; Andy, Andy; Herman, Herman; Edi, Edi; Aryanto, DidiK; Joni, Joni; Terang kita Perangin Angin, Johanes
JURNAL VOKASI TEKNIK Vol 3 No 1 (2025): JURNAL VOKASI TEKNIK (JUVOTEK)
Publisher : CV MEDAN TEKNO SOLUSI

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

Abstract

Deteksi emosional pada wajah manusia adalah bidang penting dalam pemrosesan citra dan pemahaman emosi. Penelitian ini mengembangkan aplikasi deteksi emosi menggunakan algoritma Haar Cascade Classifier untuk mendeteksi wajah dan Convolutional Neural Network (CNN) dengan optimizer Adam untuk menganalisis emosi. Aplikasi yang dikembangkan berhasil mendeteksi tujuh jenis emosi dasar (marah, jijik, takut, senang, netral, sedih, dan terkejut) secara real-time dengan akurasi keseluruhan sebesar 90%. Kombinasi CNN dan optimizer Adam menunjukkan performa yang baik dengan peningkatan akurasi dan penurunan loss yang konsisten seiring bertambahnya epoch, meskipun terdapat indikasi overfitting. Hasil penelitian menunjukkan bahwa sistem ini dapat diandalkan untuk mendeteksi berbagai ekspresi wajah dan memberikan solusi yang efektif dan akurat dalam deteksi emosional.
Permainan Monster Defence Dengan Metode Collision Detection Dan Boids Sebagai Media Edukasi Pengenalan Warna Bagi Anak-Anak Chandra; Johanes Terang Kita Perangin-Angin; Pieter Octaviandy; Robby Wijaya
Jurnal Armada Informatika Vol 8 No 1 (2024): Jurnal Armada Informatika
Publisher : STMIK Methodist Binjai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36520/jai.v8i1.94

Abstract

Monster Defence merupakan permainan yang akan terdapat sekumpulan monster dengan warna yang berbeda- beda. Untuk melakukan hal tersebut, maka dapat digunakan algoritma collision detection dan algoritma Boids. Algoritma collision detection adalah proses pengecekan apakah beberapa buah objek spasial saling bertumpuk atau tidak. Jika ditemukan setidaknya ada dua objek yang bertumpukan, artinya kedua objek 3 tersebut dikatakan saling bertumpukan dalam ruang dua dimensi. Algoritma collision detection membahas bagaimana agar seseorang tahu objek mana yang menyentuh objek lain pada bidang koordinat tertentu . Pada ruang spasial dua dimensi objek yang bertumpuk berarti objek spasialnya beririsan . Sementara itu, untuk melakukan pengaturan kecepatan dan pergerakan dari monster, maka akan digunakan metode Boids. Algoritma Boids merupakan sebuah algoritma hasil dari simulasi gerakan berkelompok dan algoritma boids memiliki 3 aturan yaitu, separation, alignment dan cohesion yang menentukan keputusan gerak random Non Player Character (NPC).
PENGARUH DIMENSI KUALITAS WEBSITE TERHADAP KEPUASAN PENGGUNA E-COMMERCE SHOPEE DI KALANGAN MAHASISWA STMIK TIME Johanes Terang Kita Perangin Angin; Bayu Teta
Manajemen: Jurnal Ekonomi USI Vol 7 No 1 (2025): Manajemen : Jurnal Ekonomi
Publisher : Fakultas Ekonomi Universitas Simalungun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36985/ax64wf71

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh dimensi kualitas website terhadap kepuasan pengguna e-commerce Shopee di kalangan mahasiswa STMIK TIME. Dimensi kualitas website yang digunakan dalam penelitian ini meliputi usability, information quality, dan interaction quality. Metode penelitian yang digunakan adalah pendekatan kuantitatif dengan analisis menggunakan Structural Equation Modelling Partial Least Square (SEM-PLS). Data dikumpulkan melalui kuesioner yang dibagikan kepada 72 responden yang dipilih menggunakan metode simple random sampling. Hasil penelitian menunjukkan bahwa usability dan information quality berpengaruh signifikan terhadap kepuasan pengguna e-commerce Shopee. Sebaliknya, interaction quality tidak berpengaruh signifikan terhadap kepuasan pengguna
Akurasi K-Means dengan Menggunakan Cluster dan Titik Grid Terbaik pada Pemetaan Grid Interatif K-Means Perangin Angin, Johanes Terang Kita; Rizkita, Ari; Robet, Robet; Pribadi, Octara
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp127-129

Abstract

Traditional K-Means face 2 (two) main problems, namely: Determination of Initial Centroid and poor initial cluster. Determining the initial centroid using random numbers is one of the main problems in classical K-Means which results in low accuracy and long computation time. Likewise, determining the good centroid of each cluster without being accompanied by a process of paying attention to the performance of each cluster can also cause the accuracy value obtained is not good. This study will contribute to how the performance obtained by determining a good initial centroid is combined with the use of a good cluster. Determination of a good initial centroid is done by using the K-Means Grid Mapping which divides the determination of the centroid into several Grid Points. The result of this research is a combination of Iterative K-Means with Grid Mapping K-Means to become Iterative Grid Mapping K-Means which will get a good initial centroid and also a good cluster shown in the table of iris and abalone, comparison of the variables in the iris and abalone affecting the best cluster as a result.
Attention Augmented Deep Learning Model for Enhanced Feature Extraction in Cacao Disease Recognition Robet, Robet; Perangin Angin, Johanes Terang Kita; Siregar, Tarq Hilmar
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15249

Abstract

Accurate cacao disease recognition is critical for safeguarding yields and reducing losses. Prior cacao studies primarily rely on handcrafted descriptors (eg, Color Histogram, LBP, GLCM) or standard CNN/transfer-learning pipelines, often limited to ≤ 3 classes and a single plant organ; explicit channel-spatial attention and comprehensive multiclass evaluation remain uncommon. To the best of our knowledge, no prior work integrates Squeeze-and-Excitation (SE) and the Convolutional Block Attention Module (CBAM) on a ResNeXt50 backbone for six-class cacao disease classification, accompanied by a standardized ablation study and t-SNE-based interpretability. We propose a six-class classifier (five diseases + healthy) built on ResNeXt-50 enhanced with SE (channel recalibration) and CBAM (channel-spatial emphasis) to highlight lesion-relevant cues. The dataset comprises labeled leaf and pod images from public sources collected under field-like conditions; preprocessing includes resizing to 224x224, normalization, and augmentation (flips, small rotations, color jitter, random resized crops). Trained with Adam and early stopping, ResNeXt50+SE+CBAM attains 97% test accuracy and 0.97 macro-F1, surpassing a ResNeXt50 baseline of 94% and 0.95 and SE-only/CBAM-only variants. Confusion matrix and t-SNE analyses show fewer mix-ups among visual classes and clearer separability, while the ablation validates complementary benefits of SE and CBAM. On a desktop-hosted, web-based setup, batch-1 inference at 224x224 is 7.46 ms/image (134 FPS), demonstrating real-time capability. The findings support deployment as browser-based decision-support tools for farmers and integration into continuous field-monitoring systems.
EVALUATION OF HYBRID MOVIE RECOMMENDATION SYSTEM BASED ON NEURAL NETWORKS Widjaja, William; Robert; Johanes Terang Kita Perangin - Angin
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.4079

Abstract

Abstract: Recommendation systems are becoming increasingly important with the growth of streaming platforms. The purpose of this study is to compare the performance of Content-Based Filtering, Neural Collaborative Filtering, and a combination of both in a movie recommendation system. The method used in this study involves retrieving movie details from the TMDB API and ratings from the MovieLens 32M Dataset (2010-2023). Each model's performance is evaluated using evaluation metrics such as RMSE and MAE. The results of this study indicate that Neural Collaborative Filtering achieves the best prediction performance (RMSE = 0.785423, MAE = 0.581262), followed by the hybrid model (RMSE = 0.800863, MAE = 0.660872), while Content-Based Filtering produces low performance and limits the capabilities of the hybrid model. In conclusion, these findings highlight the superiority of latent feature-based models such as NCF that learn directly from user interaction patterns over content-based approaches in the context of modern recommendation systems. Keywords: content-based filtering; hybrid filtering; movie recommendation; neural collaborative filtering. Abstrak: Sistem rekomendasi menjadi semakin penting seiring berkembangnya platform streaming. Tujuan dari penelitian ini adalah membandingkan kinerja Content-Based Filtering, Neural Collaborative Filtering dan kombinasi keduanya dalam sistem rekomendasi film. Metode yang digunakan dalam penelitian ini melibatkan pengambilan detail film dari TMDB API dan rating dari dataset MovieLens 32M Dataset (2010-2023). Setiap peforma model dievaluasi dengan menggunakan metrik evaluasi seperti RMSE dan MAE. Hasil dari penelitian ini menunjukkan bahwa Neural Collaborative Filtering mencapai kinerja prediksi terbaik (RMSE = 0.785423, MAE = 0.581262), diikuti oleh model hybrid (RMSE = 0.800863, MAE = 0.660872), sementara Content-Based Filtering menghasilkankan peforma yang rendah dan membatasi kemampuan model hybrid. Kesimpulannya, penelitian ini menyoroti superiotas model berbasis latent feature seperti NCF yang belajar langsung dari pola interaksi pengguna dibandingkan pendekatan berbasis konten dalam konteks sistem rekomendasi modern. Kata kunci: content-based filtering; hybrid filtering; neural collaborative filtering; rekomendasi film.
Co-Authors Andriasan Sudarso Andy Andy Bayu Teta Chandra Chandra Chandra David David Didik Aryanto edi edi Edi Wijaya Elvie Maria Feriani Astuti Tarigan Ferry Hidayat Jayawarsa, A.A. Ketut Joni Joni Octara Pribadi Pieter Octaviandy Pieter Octaviandy, Pieter Poppy Amalia, Poppy Rizkita, Ari Robert Robet Robet Robet Siregar, Tarq Hilmar Wijaya, Robby William Widjaja Wilson Wilson Wiwik Handayani