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Techno.Com: Jurnal Teknologi Informasi
Published by Universitas Dian Nuswantoro
ISSN : 14122693     EISSN : 23562579     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Topik dari jurnal Techno.Com adalah sebagai berikut (namun tidak terbatas pada topik berikut) : Digital Signal Processing, Human Computer Interaction, IT Governance, Networking Technology, Optical Communication Technology, New Media Technology, Information Search Engine, Multimedia, Computer Vision, Information Retrieval, Intelligent System, Distributed Computing System, Mobile Processing, Computer Network Security, Natural Language Processing, Business Process, Cognitive Systems, Software Engineering, Programming Methodology and Paradigm, Data Engineering, Information Management, Knowledge Based Management System, Game Technolog
Arjuna Subject : -
Articles 737 Documents
 70   71   72   73   74 
Detection and Analysis of Batik Waste Using Image Processing Methods in Pekalongan Regency Tachriri, Yusril Ihza; Agustina, Elvinda Bendra; Rachman, Dian Arif; Sari, Atika Windra; Karimah, Imroatul; Artamevira, Jessika
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14849

Abstract

Research was conducted on the detection of batik wastewater in the batik industry of Pekalongan, which generates liquid waste containing synthetic dyes, heavy metals, and hazardous compounds that can potentially pollute the environment if not properly treated. This study aims to develop a simple detection method based on digital image analysis to identify the color characteristics of batik wastewater. Data were obtained by sampling liquid waste from several affected rivers, which were then analyzed using a digital camera and image processing software to determine the intensity values of the red, green, and blue (RGB) channels. The results show that variations in waste concentration significantly influence the distribution of RGB values, enabling faster, cheaper, and more practical identification of pollution patterns compared to conventional laboratory methods. These findings are expected to serve as the foundation for developing a digital technology-based batik wastewater quality monitoring system as part of efforts to mitigate environmental pollution in Pekalongan.   Keywords - Batik wastewater, Digital image analysis, RGB intensity, Environmental pollution, Image processing
Pendekatan Backpropagation Artificial Neural Network Untuk Prediksi Kemurnian Madu Tafsir, Andi Muh Ihsanul; Sulfayanti, Sulfayanti; Nur, Nahya
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14855

Abstract

Madu merupakan produk alami yang kemurniannya menjadi indikator utama kualitas dan keasliannya. Penelitian ini bertujuan untuk memprediksi tingkat kemurnian madu menggunakan algoritma Artificial Neural Network (ANN) dengan metode Backpropagation. Dataset terdiri dari 247.903 data dengan 10 atribut madu yang digunakan sebagai variabel input, sedangkan tingkat kemurnian madu dijadikan sebagai target output. Tahapan penelitian meliputi pra-pemrosesan data, pelatihan model, serta evaluasi hasil prediksi. Setelah melalui tahap pra-pemrosesan, jumlah fitur input bertambah menjadi 27. Pada proses eksperimen, dilakukan pengujian beberapa variasi arsitektur (27-14-14-1, 27-27-27-1, 27-54-54-1), fungsi aktivasi (ReLU, sigmoid biner, sigmoid bipolar), learning rate (0,01, 0,1, 0,5), dan jumlah epoch (1000, 1500, 2000) untuk memperoleh konfigurasi terbaik. Hasil optimal diperoleh pada arsitektur jaringan 27-54-54-1 dengan fungsi aktivasi ReLU, learning rate 0,5, dan jumlah epoch sebanyak 2000. Konfigurasi tersebut menghasilkan kinerja prediksi dengan nilai Mean Squared Error (MSE) 0,000542, R-squared (R²) sebesar 0,972010, dan Mean Absolute Percentage Error (MAPE) 1,26%. Hasil ini membuktikan bahwa algoritma Backpropagation Artificial Neural Network dapat digunakan secara efektif dalam memprediksi tingkat kemurnian madu. Kata Kunci - Artificial Neural Network, Backpropagation, Prediksi, Kemurnian Madu
Penerapan Metode UEQ dan Importance Performance Analysis dalam Evaluasi User Experience Layanan Pembayaran Akademik Ramadhan, Elang Safamoza; Nuryasin, Ilyas; Wiyono, Briansyah Setio
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14874

Abstract

Untuk memastikan layanan pembayaran akademik memenuhi ekspektasi pengguna, evaluasi kualitas pengalaman pengguna atau user experience (UX) wajib dilakukan. Penelitian ini menggunakan metode User Experience Questionnaire (UEQ) yang mencakup enam dimensi penilaian yaitu Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, dan Novelty. Selanjutnya, metode Importance Performance Analysis (IPA) diterapkan untuk memetakan prioritas perbaikan, didukung oleh Key Performance Indicator (KPI) dari UEQ. Layanan pembayaran InfoKHS Universitas Muhammadiyah Malang (UMM) digunakan sebagai objek penelitian. Sebanyak 38 dari 61 data responden valid digunakan setelah proses eliminasi data inkonsisten. Hasil UEQ menunjukkan penilaian positif pada lima dimensi (kecuali Novelty yang netral). Berdasarkan benchmark UEQ, terdapat empat dimensi yang dinilai di atas rata-rata, kecuali Stimulation dan Novelty. Analisis IPA menempatkan Perspicuity, Efficiency, dan Dependability pada Kuadran 1 untuk dipertahankan kinerjanya. Dimensi Attractiveness berada di Kuadran 2 yang memiliki performa berlebihan, sementara Stimulation dan Novelty masuk Kuadran 3 dengan prioritas perbaikan rendah. Kesimpulan penelitian ini adalah bahwa sistem layanan pembayaran InfoKHS UMM secara umum telah mencapai pengalaman pengguna yang baik. Namun, aspek Stimulation dan Novelty masih perlu diperhatikan untuk peningkatan kualitas sistem yang berkelanjutan.   Kata Kunci - User Experience Questionnaire, UEQ, Importance Performance Analysis, KPI, Universitas Muhammadiyah Malang
Association Pattern Analysis of Global Company Market Capitalization Using the FP-Growth Algorithm with Load Balancing Constraint Adam, Stenly Ibrahim; Pungus, Stenly Richard; Mokodaser, Wilsen Grivin
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14885

Abstract

This research focuses on analyzing the global company market capitalization dataset using the FP-Growth algorithm combined with a load-balancing constraint approach. The main objective is to identify association patterns among different market capitalization categories Small, Medium, Large, Mega, and Ultra to understand their distribution and interrelationships. The study begins with data preprocessing, cleaning, and categorization of companies based on their market values. The FP-Growth algorithm is applied with a minimum support threshold of 0.02, and a load balancing constraint is introduced by filtering rules with support ≥ 0.05 and lift > 1, ensuring balanced and significant association patterns. The analysis results show that the most dominant categories are Medium and Small, representing the majority of companies worldwide, while Large, Mega, and Ultra categories are relatively rare. The strongest rule indicates that countries with “Large” companies are very likely to also have “Small” and “Medium” companies. Evaluation metrics show an average lift of 1.171 and an average confidence of 1.000, confirming strong and reliable associations. Overall, this study provides insights into global market capitalization patterns and demonstrates the effectiveness of FP-Growth with constraints in revealing meaningful, balanced relationships within large-scale business data.   Keywords – FP-Growth, Load Balancing Constraint, Market Capitalization, Association.
YouTube Comment Clustering Using K-Means in A Case Study of The Indonesian New Capital City (IKN) Nova, Sausan Hidayah; Rizki, Afian Syafaadi; Wibowo, Dwi Agung; Ridha, M Najamudin; Karima, Cahya; Permatasari, Nindy
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14905

Abstract

The relocation of the capital city of the Republic of Indonesia from Jakarta to the Nusantara Capital City (IKN) is a critical topic for the public, as it is designated as a strategic national project. However, the lack of public participation may generate community concerns regarding its potential impact. This research involved extracting public opinion from YouTube comments to identify the community’s desires, thereby providing policymakers with valuable information. Clustering the comments using the K-Means method successfully extracted public opinions from 27,063 comment data points. Among the key findings, a significant public concern is the potential for the construction project to be abandoned or stalled (“mangkrak”). Additionally, while the clustering results showed good cohesion, the cluster separation indicated a significant overlap in the data. This is further reflected by the average similarity score of 0.4234972.   Keywords – YouTube, Text Clustering, K-Means, Nusantara Capital City (IKN)
Penggunaan Algoritma K-Means dalam Mengelompokkan Jumlah Usaha dan Hasil Pendapatan UMKM DKI Jakarta Sunarti, Sunarti; Alawiah, Enok Tuti; Pahlevi, Omar
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14925

Abstract

UMKM Provinsi DKI Jakarta berkontribusi signifikan terhadap perekonomian daerah. Namun, ada permasalahannya pemetaan data lemah, segmentasi usaha kurang, belum adanya pemetaan yang jelas mengenai jumlah usaha dan pendapatan, keterbatasan akses data dan validitas informasi, serta belum adanya diferensiasi program pembangunan berdasarkan karakteristik usaha. Tujuan penelitian adalah menganalisis data jumlah usaha dan hasil pendapatan UMKM menggunakan metode K-means. Metode ini digunakan mengelompokkan jumlah usaha dan hasil pendapatan. Hasil klasterisasi menghasilkan tiga klaster wilayah: Klaster (1) yaitu kota administratif Jakarta Pusat, Jakarta Selatan, dan Jakarta Timur, memiliki jumlah usaha dan pendapatan tertinggi. Klaster(2) yaitu kota administratif Jakarta Barat dan Jakarta Utara, memiliki jumlah usaha dan pendapatan sedang. Klaster(3) yaitu kota administratif Kepulauan Seribu, memiliki jumlah usaha dan pendapatan yang rendah. Hasil evaluasi proses klasterisasi mempergunakan Davies Bouldin Index (DBI) bernilai -0.390. Hasil ini menunjukkan K-Means dapat memetakan jumlah usaha dan hasil pendapatan, sehingga memudahkan dalam menetapkan strategis kebijakan, mengembangkan usaha, dan menyusun strategi pemasaran berdasarkan karakteristik ekonomi setiap daerah.   Kata Kunci – UMKM, Provinsi DKI Jakarta, Klasterisasi, Metode K-Means
A Co-Design-Based Development Model for an Adaptive Learning System: A Case Study on Enhancing Digital Science Literacy for Junior High School Students Munaji, Achmad Arif; Abdurrahman , Abdurrahman; Alya, Nor; Aisyah , Nor
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14937

Abstract

This paper details the development and empirical evaluation of an adaptive learning system aimed at enhancing digital science literacy among junior high school students in Indonesia. The primary challenge addressed is the limitation of one-size-fits-all educational models. Our research proposes a user-centric solution, the Sistem Rekomendasi Cerdas (SRC), developed through a co-design methodology. The system’s core is a User-based Collaborative Filtering (UBCF) algorithm. Its effectiveness was evaluated through a pre-test/post-test experimental study involving 60 students, divided into an experimental and a control group. Quantitative results show that the experimental group achieved a significantly higher increase in science literacy scores (p < 0.001) compared to the control group. Qualitative findings from interviews with the experimental group reveal that the platform enhanced learning motivation, content relevance, and helped overcome learning barriers. This study concludes that the SRC, developed via a co-design model, is a highly effective tool for improving digital science literacy, demonstrating that a user-centered approach is fundamental to creating impactful educational technology.   Keywords - Adaptive Learning, Co-Design, Recommender System, Digital Literacy, Usability.
Evaluasi Kinerja Algoritma Apriori dan FP-Growth untuk Association Rule Mining pada Data Transaksi Ritel Soewignyo, Fanny; Soewignyo, Tonny Irianto; Mokodaser, Wilsen Grivin; Silitonga, Argha Orion
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14952

Abstract

Ledakan data transaksi ritel yang terekam melalui sistem Point of Sale (POS) dan platform daring menuntut metode analisis yang efektif untuk menggali pola pembelian konsumen. Association Rule Mining merupakan pendekatan populer untuk menemukan keterkaitan antarproduk, dengan algoritma Apriori dan FP-Growth sebagai dua metode yang paling banyak digunakan. Penelitian ini bertujuan memberikan gambaran empiris mengenai efektivitas kedua algoritma tersebut pada data transaksi ritel yang nyata. Metode yang digunakan meliputi tahapan data understanding untuk mengenali struktur data, data cleaning untuk menghapus nilai kosong dan menyeragamkan format, serta data transformation menggunakan TransactionEncoder untuk mengubah data mentah menjadi format biner (one-hot encoded). Selanjutnya algoritma Apriori dan FP-Growth dijalankan dengan parameter yang sama untuk menghasilkan frequent itemsets dan aturan asosiasi. Evaluasi kinerja dilakukan dengan mengukur waktu pemrosesan, jumlah aturan yang dihasilkan, serta nilai support, confidence, dan lift tertinggi. Hasil penelitian menunjukkan bahwa kedua algoritma menghasilkan jumlah aturan yang sama (63 aturan) dengan support tertinggi 0,06, confidence tertinggi 0,51, dan lift tertinggi 3,29, tetapi waktu pemrosesan berbeda signifikan (Apriori 0,39 detik, FP-Growth 6,95 detik). Kesimpulannya, association rule mining efektif mengungkap pola pembelian konsumen, dan algoritma Apriori lebih efisien untuk dataset kecil hingga menengah, sedangkan FP-Growth lebih sesuai untuk dataset yang jauh lebih besar. Keywords - Association Rules, Apriori, FP-Growth, Frequent Itemset, Transaksi Ritel.
Voltage, Current, and Work Process Monitoring System on CNC Diode Lasers Zulhijjah, Intan Dwi; Fitriani, Endah; Paramytha IS, Nina; Dasmen, Rahmat Novrianda
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14961

Abstract

The advancement of CNC laser diode technology has transformed manufacturing by enabling high precision cutting and engraving of various materials, yet ensuring reliable operation requires accurate monitoring of electrical and mechanical parameters. This research focused on designing and implementing a real-time monitoring system that integrates the PZEM-004T sensor for electrical measurements and limit switches for safety boundaries in CNC laser diode machines. The system was developed using NodeMCU ESP8266 as the main controller, with results displayed on an LCD 16x2 and alarms generated through a buzzer and relay. Experimental evaluation demonstrated that the system effectively measured voltage, current, power, and energy, with the PZEM-004T showing an average error below 5% compared to reference instruments. The integration of limit switches functioned reliably, preventing mechanical failures and ensuring safe operation within defined limits. Monitoring data supported energy efficiency by identifying power usage patterns and reducing operational risks. Engraving tests confirmed stable performance, with improved detail observed during longer process durations. Overall, the implemented system provides a practical and reliable solution for operational monitoring and mechanical protection, making it particularly suitable for small-scale CNC laser diode applications in industrial, educational, and creative fields.
A Comparative Analysis of Deep Learning Models for Knee Osteoarthritis Severity Grading Mulijono, Steffany Florence Sugiarto; Wonohadidjojo, Daniel Martomanggolo
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.14967

Abstract

The Kellgren-Lawrence (KL) grading system is commonly used to evaluate knee osteoarthritis (OA), but it can be subjective and subject to variation among assessors. Our study looked at three Convolutional Neural Network (CNN) methods for OA severity classification from a dataset of 15,770 X-ray images to overcome this difficulty and create a more objective technique. Under the same preprocessing conditions, we contrasted a baseline custom CNN, DenseNet201, and a hybrid model with a CBAM attention mechanism. With an overall accuracy of 65%, a weighted precision and recall of 65%, and an F1-score of 64%, the hybrid model, which uses DenseNet201 as a fixed feature extractor, performed the best. This was better than both the baseline model (59% accuracy) and the standalone DenseNet201 (59% accuracy). Although the hybrid architecture has a lot of promise, we also had to deal with issues like overfitting. Our thorough comparison demonstrates how this hybrid strategy can successfully combine strong pre-trained features with the flexibility required for particular tasks. Although more clinical validation is necessary, this shows that automated systems like ours could improve diagnostic consistency in OA grading.   Keywords - Knee Osteoarthritis, Kellgren-Lawrence Grading, Deep Learning, Attention Mechanism, CBAM

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