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All Journal Jurnal Transformatika Teknik: Jurnal Ilmu Teknik dan Informatika Journal Of Human And Education (JAHE) Jurnal Teknik Informatika dan Teknologi Informasi Journal of Technology Informatics and Engineering Jurnal Ilmiah Sistem Informasi
Supriadi, Candra
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Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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SISTEM INFORMASI PENGHITUNGAN HASIL PRODUK BERBASIS INTERNET OF THINGS Abdul Khalim, Muhammad; Heri Kurniawan, Andreas; Supriadi, Candra
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 2 No. 1 (2022): April: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (300.136 KB) | DOI: 10.55606/jutiti.v2i1.369

Abstract

Perkembangan teknologi saat ini sangat pesat, hal ini dapat dibuktikan dengan banyaknya alat-alat yang diciptakan manusia untuk mempermudah dalam kehidupan. Didalam industri berbagai macam pekerjaan dilakukan dengan cepat sehingga dengan counter yang bersifat manual akan menghambat seperti barang-barang.Pada PT. APPAREL ONE INDONESIA untuk menghitung sebuah outputan harus di lakukan dengan manual atau dengan menekan satu persatu, maka dari itu peneliti memecahkan solusi dengan membuat sistem penghitungan barang otomatis berbasis Internet of Things (IoT). Sistem utama dirancang menggunakan Sensor ultrasonik,sensor berat dan Nodemcu esp8266. Dengan metode prototype untuk mensimulasikan sistem ini sebelum di terapkan di lapangan produksi untuk mengetahui cara kerja sistem. Berdasarkan pengujian dengan cara object didekatkan dengan sensor ultrasonik dan berat untuk mengetahui jumlah barang secara otomatis yang dihasilkan setiap harinya, tanpa harus melakukan penghitungan secara manual. Selain penghitungan otomatis, sistem ini juga melakukan penginputan secara otomatis dan data tersebut tersimpan di database
Pelatihan Teknik Pembuatan Konten Menarik dan Informatif Untuk Media Sosial Bagi Penggiat Literasi Digital Desa Gladagsari, Boyolali Dewi, Maya Utami; Nugroho, Aris Sarwo; Kholifah, Siti; Siswanto, Siswanto; Sumaryanto, Sumaryanto; Fitrianto, Yuli; Qosidah, Nanik; Nurmana, Ayyub Hamdanu Budi; Supriadi, Candra; Imaliya, Tri
Journal Of Human And Education (JAHE) Vol. 5 No. 1 (2025): Journal of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v5i1.2131

Abstract

In this increasingly advanced digital era, social media has become the leading platform for many individuals and organizations to convey messages and information to a broad audience. The training activity on interesting and informative content creation techniques for social media for digital literacy activists in Gladagsari Village, Boyolali, aims to equip participants with the knowledge and skills needed to create content that not only attracts attention but also provides high informative value and relevance. Good content is not only able to attract the attention of the audience but is also able to convey messages clearly and increase interaction and engagement. This training was attended by 15 participants and was held in Gondang Village, Ampel District, Boyolali Regency. The training uses theoretical and practical delivery techniques. The training material focuses on creating interesting and innovative content for social media platforms with copywriting techniques. The results of the training showed satisfactory results with an increase in participants' ability to create content on social media with a value of 78%, which is included in the good category.
Sensitivitas Sistem Pencarian Artikel Bahasa Indonesia Menggunakan Metode n-gram Dan Tanimoto Cosine Supriadi, Candra; Purnomo, Hidriyanto Dwi; Sembiring, Irwan
Jurnal Transformatika Vol. 18 No. 1 (2020): July 2020
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v18i1.2184

Abstract

The human need for technology and the availability of adequate infrastructure is evidence that technology is now a part of basic human needs. The increasing number of journals and scientific papers, it must be more selective in selecting and sorting even though there are already many online service providers and journal portals. Research on search engines and plagiarism and recommendation systems has been carried out with various methods deemed appropriate to improve the performance of the system itself, this paper has the purpose of calculating the similarity between one article with another article by implementing n-gram and tanimoto cosine. The number of articles tested was forty-three titles and abstracts, tested fifty times with randomly selected keywords, by breaking down each title and abstract sentence into n characters (n = 2 to 8) including spaces and punctuation, then counted similarity with the query or keyword used for system testing. The test was conducted using several threshold variations from n = 2 to 8. After observing fifty times the threshold test of 0.15 has the highest accuracy at n = 4 at 0.92, the highest precision at n = 3 at 0.42 and the highest recall at the test n = 2 = 0.44 .
Decentralized AI on The Edge: Implementing Federated Learning for Predictive Maintenance in Industrial IoT Systems Supriadi, Candra; Wahyudi , Wiwid; Priyadi, Agus; Jin, Kim So
Journal of Technology Informatics and Engineering Vol. 4 No. 2 (2025): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i2.281

Abstract

The integration of Artificial Intelligence (AI) into Industrial Internet of Things (IIoT) systems has enhanced predictive maintenance strategies by enabling early detection of faults in machinery. However, centralized AI models often face challenges related to data privacy, latency, and communication overhead in industrial environments. This study aims to develop a decentralized AI framework utilizing Federated Learning (FL) on edge devices to enhance predictive maintenance in a medium-scale manufacturing plant. The proposed system enables local edge nodes to collaboratively train machine learning models without sharing raw data, thereby preserving data privacy and reducing network load. A prototype was developed using embedded edge devices integrated with vibration and temperature sensors to detect machine anomalies. Federated averaging was used to aggregate local models into a global model. Experimental results show that the federated model achieved 91.4% accuracy in anomaly detection, comparable to centralized approaches, while significantly reducing data transmission volume by 68%. This research demonstrates the feasibility of deploying federated learning on resource-constrained edge devices for predictive maintenance in IIoT environments. The findings suggest that decentralized AI at the edge can offer efficient, privacy-preserving, and scalable solutions for industrial applications
Rancang Bangun Sistem Monitoring Deteksi Dini Kebakaran Ruang Server Berbasis Iot Ardiyanto, Tri; Candra Supriadi; Priyadi; Supriadi, Candra
Jurnal Ilmiah Sistem Informasi Vol. 3 No. 3 (2024): November : Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/432zjf74

Abstract

Penelitian ini bertujuan untuk merancang sistem monitoring deteksi dini kebakaran pada ruang server. Kebakaran merupakan ancaman serius yang dapat mengakibatkan kerusakan fatal pada peralatan di ruang server dan potensi kehilangan data pada storage. Untuk menunjang penelitian yang memanfaatkan otomatisasi dan hal yang bersifat real time, maka diperlukan komunikasi antara sensor-sensor dan software yang juga disebut teknologi Internet of Things (IoT). Dalam penelitian ini, sensor-sensor yang dimaksud yaitu Flame Sensor yang berguna sebagai pendeteksi radiasi dari nyala api, Sensor MQ 2 sebagai  pendeteksi asap dan Sensor DHT 11 untuk mendeteksi perubahan suhu yang  signifikan. Untuk mempermudah dalam pemantauan, juga di lengkapi dengan laman web server yang dapat diakses dengan mudah serta menggunakan beberapa output, antara lain buzzer sebagai sirine bahaya, pilot lamp untuk memberikan sinyal visual adanya suhu yang tidak normal serta outpur exhaust fan untuk membantu sirkulasi udara di dalam ruang server. Implementasi dalam rancang bangun sistem monitoring ini diharapkan dapat membantu memastikan performa server dalamkondisi yang selalu optimal dan mengurangi resiko kebakaran yang lebih fatal serta dapat mengurangi beban kerja staf pengelola di Dinas Kependudukan dan Pencatatan Sipil Kabupaten Semarang.
Adaptive Fuzzy Logic Integration for Optimizing Decision Support Systems under Data Uncertainty Supriadi, Candra
Teknik: Jurnal Ilmu Teknik dan Informatika Vol. 5 No. 2 (2025): Oktober : Teknik: Jurnal Ilmu Teknik dan Informatika
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/teknik.v5i2.1026

Abstract

Decision Support Systems (DSS) can become inaccurate when used with imprecise, incomplete, or dynamically changing data. Fuzzy logic techniques based on conventional methodologies may be strong at handling vagueness, but are unable to adapt their behavior in response to different data distributions on their own. This paper recommends the creation of an Adaptive Fuzzy Logic Integration Framework that dynamically updates membership functions and rule weights in response to data variation to enhance decision accuracy under uncertainty. The described framework combines Fuzzy Inference Systems (FIS) with learning-based parameter update concepts borrowed from adaptive optimisation. The model was simulated and executed on a hybrid algorithmic platform that included gradient-based parameter tuning and iterative feedback learning. Experimental tests were conducted on uncertainty-generated data sets to compare adaptive and conventional fuzzy models in terms of ISME (Root Mean Square Error), convergence stability, and decision accuracy. Previous results show that the adaptive model achieves a 21.4% increase in accuracy and a 28% improvement in convergence rate compared to non-adaptive fuzzy systems. Moreover, the model ensures stable performance even in the presence of random data perturbations, demonstrating its ability to handle uncertainty. This book incorporates a self-tuning fuzzy decision model that converts static inference structures to dynamic evolving decision engines. The outcomes establish a foundation for next-generation smart DSS for real-time optimization in uncertainty.
Co-Authors Abdul Khalim, Muhammad Agus Priyadi ARDIYANTO, TRI Candra Supriadi Heri Kurniawan, Andreas Imaliya, Tri Irwan Sembiring Jin, Kim So Maya Utami Dewi, Maya Utami Nugroho, Aris Sarwo Nurmana, Ayyub Hamdanu Budi Priyadi Priyadi Purnomo, Hidriyanto Dwi Qosidah, Nanik S Siswanto SITI KHOLIFAH Sumaryanto Sumaryanto, Sumaryanto Wahyudi , Wiwid Yuli Fitrianto