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All Journal Mechanova International Journal of New Media Technology Journal of Applied Data Sciences
Edwin Kurniawan
ICT Faculty, University Of Multimedia Nusantara, Tangerang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mechanical Engineering

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PENELITIAN FILTER UDARA MOBIL MENGGUNAKAN SABUT KELAPA KERING SEBAGAI PENGGANTI BAHAN FILTER UDARA AFTERMARKET Edwin Kurniawan; Fandi Dwiputra Suprianto
Mechanova Vol 5 (2016): Semester genap 2016-2017
Publisher : Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (744.017 KB)

Abstract

Seiring perkembangan teknologi kendaraan dan semakin buruknya kualitas udara di kota – kotakebutuhan alat filterasi dengan kualitas bagus semakin dibutuhkan. Filter udara sangat dibutuhkanpada tempat yang tingkat polusi udaranya tinggi atau pada kota – kota yang sedang berkembang.Salah satu kegunaan filter udara yaitu untuk menyerap dengan baik kotran dan debu. Untuk itudiperlukan bahan filter udara yang bagus sehingga proses penyaringan polusi dapat dilakukandengan baik, tetapi juga tidak membuat performa mesin menurun. Proses pembuatan bahan filterudara baru yang dapat meningkatkan proses penyaringan lebih baik dari yang sudah ada dipasaran saat ini. Proses ini melakukan pembuatan bahan filter udara dengan serabut kelapakering. Serabut kelapa dipakai, karena limbah serabut kelapa yang dapat di daur ulang menjadibarang yang lebih berguna. Proses ini hanya mengganti bagian bahan filter udara standart denganserabut kelapa dengan massa jenis 27 mg/cm3, 36 mg/cm3, dan 45 mg/cm3. Penelitian ini jugamenggunakan alat uji yang akan dibuat sesuai dengan kondisi nyata pada intake kendaraan.Dengan adanya penelitian ini diharapkan kualitas penyerapan debu dapat meningkat dan tidakmenurunkan performa kendaraan.
Implementation of Topsis Method In Web Based System Recommendations For Students Laptop Selection (Case Study: Bhinneka.com) Adhi Kusnadi; Edwin Kurniawan
IJNMT (International Journal of New Media Technology) Vol 4 No 1 (2017): IJNMT International Journal of New Media Technology
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3583.76 KB) | DOI: 10.31937/ijnmt.v4i1.537

Abstract

Computer needs at work are very helpful and make easier for human to complete his work. The usageof laptops growing rapidly with increasing mobility of the community who uses laptop to run the activities. In addition, laptop continues to update its technology with the variation of the specification so that it can attract the interest of consumers, especially for students, but the variety of laptops is confusing for some users to choose. Based on a survey that has been done to the students, it concluded that laptop is 80% more interesting than desktop computer. Some students really need a recommendation system for choosing alaptop. In making a recommendation system, using TOPSIS method is the recommended one because the concept is simple, easy to understand, efficient, and have the ability to measure the relative performance of alternatives decision. According to the implementation, the recommendation system with TOPSIS method has 70% accuracy rate. Index Terms—System Recommendation, TOPSIS, Laptop, REFERENCES [1] Hendra, dkk. 2007. “Keluhan Kesehatan pada Penggunaan Laptop pada Mahasiswa FKM UI”. (pdf). Depok: Universitas Indonesia. [2] Paays, N. 2013. Rancang Bangun Meja Laptop Yang Adjustable Berdasarkan Aspek Ergonomi. Pontianak: Universitas Tanjungpura. [3] Iqbal, M. 2014. Perilaku Pembelian Laptop Oleh Mahasiswa Strata 1 Universitas Brawijaya Malang. S1 thesis. Malang: Universitas Brawijaya. [4] Setiawan, H. 2014. Rancang Bangun Aplikasi Rekomendasi Pembelian Laptop Dengan Metode Fuzzy Database Tahani Model Berbasis Web (Studi Kasus: Toko Ricky Komputer). S1 thesis. Tangerang: Universitas Multimedia Nusantara. [5] Fitriani, S. A. 2014. Perbandingan Metode Weighted Product dengan Metode Technique for Order Preference by Similarity to Ideal Solution dalam Pendukung Keputusan Perekruten Siswa/Mahasiswa Praktek Kerja Lapangan. S1 thesis. Bandung : Universitas Pendidikan Indonesia. [6] Buaton, R.2014. 15 Metode Menyelesaikan Data Mining, Sistem Pakar, dan Sistem Pendukung Keputusan (.pdf). Medan. [7] Salehah, A. 2014. Penerapan TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) Untuk Seleksi Penerimaan Beasiswa (Studi Kasus Pendaftar Beasiswa dari Fakultas MIPA di Universitas Brawijaya). S1 thesis. Malang : Universitas Brawijaya. [8] Gay, L.R. dan Diehl, P.L. (1992), Research Methods for Business and. Management, MacMillan Publishing Company, New York
Improving Publishing: Extracting Keywords and Clustering Topics Soekamto, Yosua Setyawan; Maryati, Indra; Christian, Christian; Kurniawan, Edwin
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.199

Abstract

Humans, by nature, are inclined to share knowledge across various platforms, such as educational institutions, media outlets, and specialized research publications like journals and conferences. The consistent oversight and evaluation of these publications by ranking bodies serve to maintain the integrity and quality of scholarly discourse on a global scale. However, there has been a decline in the proliferation of such publications in recent times, partly attributed to ethical misconduct within specific segments of the scholarly community. Despite implementing systems such as the Open Journal System (OJS), publishers grapple with the formidable task of managing editorial and review processes. Compounding the multifaceted nature of scholarly content, manual review procedures often lead to considerable time investment. Thus, a pressing need exists for advanced technological solutions to streamline the article selection process, empowering publishers to prioritize articles for review based on topical relevance. This study advocates adopting a comprehensive framework integrating advanced text analysis techniques such as keyword extraction, topic clustering, and summarization algorithms. These tools can be implemented and integrated by connecting with the database of the existing system. By leveraging these tools with the expertise of editorial and review teams, publishers can significantly expedite the initial assessment of submitted articles. Given the rapid technological advancements, publishers must embrace robust systems that enhance efficiency and effectiveness, particularly in reviewer assignments and article prioritization. This research employs the neural network approach of BERT and K-Means clustering to perform keyword extraction and topic clustering. Furthermore, using BERT facilitates accurate semantic understanding and context-aware representation of textual data. Additionally, BERT's pre-trained models enable its fine-tuning capability to allow customization to specific domains or tasks. By harnessing the power of BERT, publishers can gain deeper insights into the content of scholarly articles, leading to more informed decision-making and improved publication outcomes.
Nature-based Hyperparameter Tuning of a Multilayer Perceptron Algorithm in Task Classification: A Case Study on Fear of Failure in Entrepreneurship Saputri, Theresia Ratih Dewi; Kurniawan, Edwin; Lestari, Caecilia Citra; Antonio, Tony
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.539

Abstract

Entrepreneurship plays a key role in generating economic growth, encouraging innovation, and creating job opportunities. Understanding which demographic, psychological, and socio-economic factors contribute to fear of failure in entrepreneurship is essential to developing proper standards in entrepreneurship education and policy. However, it remains challenging to accurately classify these factors, especially when balancing model performance with model complexity in a multilayer perceptron algorithm. An effective model requires the correct parameter setting via a hyperparameter tuning process. Adjusting each hyperparameter by hand requires significant effort and knowledge, as there are frequently multiple combinations to consider. Furthermore, manual tuning is prone to human error and may overlook optimal configurations, resulting in inferior model performance and prediction accuracy. This study evaluates nature-inspired optimization techniques, including particle swarm optimization (PSO), genetic algorithm (GA), and grey wolf optimization (GWO). Several parameters are tuned in the present multilayer perceptron model, including the number of hidden layers and the number of nodes in each hidden layer, learning rate, and activation functions. The used dataset which consists of 39 features from 333 samples captured individual fears, loss score, and computational efficiency as the required amount of time for finding the best parameter combination. Model accuracy performance scores are 45.16%, 53.76%, and 58.61% for GA, PSO, and GWO, respectively. Meanwhile their execution time are 10 minutes, 27 minutes, and 23 minutes, for GA, PSO, and GWO, respectively. Experiment results further reveal that each optimization algorithm has distinct advantages: GA excels at speedy convergence, PSO provides a robust exploration of hyperparameter space, and GWO offers remarkable adaptability to complicated parameter interdependencies. This study provides empirical evidence for the efficacy of nature-inspired hyperparameter modification in improving multilayer perceptron performance for fear of failure categorization tasks.
Co-Authors Adhi Kusnadi christian christian Fandi Dwiputra Suprianto Indra Maryati Lestari, Caecilia Citra Theresia Ratih Dewi Saputri Tony Antonio Yosua Setyawan Soekamto