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All Journal International Journal of Electrical and Computer Engineering Jurnal Computech & Bisnis (e-Journal) Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JRSI (Jurnal Rekayasa Sistem dan Industri) JURNAL MEDIA INFORMATIKA BUDIDARMA JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan JMECS (Journal of Measurements, Electronics, Communications, and Systems) Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Reswara: Jurnal Pengabdian Kepada Masyarakat eProceedings of Engineering
Ibnu Asror
Universitas Telkom
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Klasifikasi Tingkat Kualitas Udara DKI Jakarta Berdasarkan Open Government Data Menggunakan Algoritma Random Forest Adityo Nugroho; Ibnu Asror; Yanuar Firdaus Arie Wibowo
eProceedings of Engineering Vol 10, No 2 (2023): April 2023
Publisher : eProceedings of Engineering

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

Abstract

Abstrak-Kualitas udara pada kota yang memiliki padat penduduk tinggi dengan banyaknya pabrik industri serta padatnya jalan raya oleh kendaraan seperti DKI Jakarta harus diperhatikan kondisinya. Terdapat banyak data yang muncul mengenai kualitas udara di DKI Jakarta yang selalu menurun disebabkan oleh pencemaran udara. Data yang di dapatkan berasal dari website Jakarta Open Data yang menampilkan Indeks Standar Penecamaran Udara (ISPU) pada setiap harinya. Metode yang digunakan yaitu data mining klasifikasi, karena metode tersebut dapat digunakan untuk mengetahui informasi mengenai pencemaran udara berdasarkan pengolahan data parameter yang ada pada ISPU. Klasifikasi yang dilakukan yaitu menggunakan algoritma random forest. Membuat sebuah model uji pada klasifikasi menggunakan random forest bertujuan untuk mencari hasil terbaik. Hasil dari klasifikasi berdasarkan data Indeks Pencemaran Udara di DKI Jakarta memiliki performa terbaik yang menghasilkan akurasi tertinggi yaitu 90%. Pada kategori Sedang memiliki nilai precision 90,9%, recall 89,28% f1-score 90,09%, dan categori Tidak Sehat memiliki nilai precision 89,09%, recall 90,74%, dan f1-score 89,9%.Kata kunci- kualitas udara, klasifikasi, random forest
Analyzing schools admission performance achievement using hierarchical clustering Fahrudin, Tora; Asror, Ibnu; Wibowo, Yanuar Firdaus Arie
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5566-5584

Abstract

In this study, an implementation of hierarchical clustering methods was conducted in schools’ admission data. We aim to demonstrate that the hierarchical clustering method can be used to help analyze the membership changes of each cluster based on its achievement number of new students from different months period observations. This method can be used by decision-makers to make a strategy for each school which has decreasing achievement from the previous period. In this paper, we employ the hierarchical clustering method to cluster admission performance achievement from fifty Telkom Schools. Instead of clustering admission in one period directly, this paper tried to analyze the movement of clustering membership from one period to another. We observed the movement membership of the group from three categories period, such as monthly, quarterly, and semesterly. The experimental results demonstrate that the monthly scenario was the best clustering result. The monthly scenario achieves the best score for all metrics such as the Dunn index, Silhouette score, Davies-Bouldin index, and Calinski-Harabasz compared to the quarter and semester scenario. There are four schools which are consistent in the first cluster and seven schools which are consistent in the second cluster in all scenarios and all periods.
PELATIHAN PENGGUNAAN APLIKASI DISEMINASI INFORMASI DI KPT TELKOM MAKASSAR Fahrudin, Tora; Asror, Ibnu; Suwawi, Dawam Dwi Jatmiko; Wibowo, Yanuar Firdaus Arie
Reswara: Jurnal Pengabdian Kepada Masyarakat Vol 4, No 1 (2023)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v4i1.2264

Abstract

Kegiatan pelatihan yang diselenggarakan di Kelompok Pendidikan Terpadu Telkom Makassar ini adalah program pengabdian pada masyarakat yang bertujuan untuk membantu pihak pengelola pendidikan Telkom Schools dalam memanfaatkan penggunaan WhatsApp (WA) sebagai sarana penyebaran informasi kepada sivitas akademika yg didapatkan melalui assesstment awal permasalahan mitra dalam mendiseminasikan informasi. Dengan menggunakan aplikasi penyebaran informasi melalui WA ini, diharapkan informasi-informasi penting dan sifatnya dinamis dapat sampai dalam waktu yang singkat dan efektif kepada orang tua siswa maupun siswa. Selain melalui aplikasi, aplikasi ini dilengkapi dengan Application Programming Interface (API) sehingga memungkinkan pengiriman informasi dapat diintegrasikan dengan aplikasi-aplikasi yang lain seperti Sistem Informasi Akademik maupun Sistem Informasi Perpustakaan. Pelaksanaan kegiatan pengabdian masyarakat ini membutuhkan waktu lebih kurang enam bulan, yang meliputi observasi dan diskusi awal, pembuatan aplikasi, pelatihan penggunaan aplikasi, pembuatan video dokumentasi, serta pembuatan laporan akhir. Pelatihan diberikan kepada kepala sekolah maupun admin masing-masing sekolah melalui aplikasi zoom. Hasil dari pengabdian masyarakat ini diperoleh feedback pelaksanaan pelatihan yaitu sebesar 60% setuju dan 40% sangat setuju bahwa materi yang disampaikan sesuai dengan kebutuhan mitra. Selain itu sebesar 53.33% setuju dan 46.67% sangat setuju bahwa teknologi yang ditawarkan sangat bermaanfaat. Terkait dengan kemudahan penyajian materi sebesar 53.33% menyatakan setuju dan 46.67% setuju
Interest Classification on Named Data Network Using the Supervised Learning Method Astuti, Sri; Mayasari, Ratna; Asror, Ibnu
JMECS (Journal of Measurements, Electronics, Communications, and Systems) Vol. 11 No. 1 (2024): JMECS
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v11i1.8100

Abstract

Named Data Network (NDN) is a next-generation network architecture that shifts the traditional data communications paradigm Unlike conventional networks that rely on IP addresses, NDN delivers content based on data names rather than specific locations. In NDN, consumers express their requests by sending interest packets containing content names. These names are then propagated through the network nodes, which forward them to the appropriate destinations. The forwarding strategy in an NDN network plays a crucial role in ensuring efficient data delivery. This strategy includes a set of rules that determine the next hop for each interest packet. These rules are designed to optimize the forwarding process, minimizing delays and improving network efficiency. However, if the forwarding strategy is implemented without accurately identifying the appropriate face (i.e., the network interface) to forward interests toward the producer or the nearest cache node, it can lead to significant delays and packet drops. This, in turn, negatively impacts Quality of Service (QoS) parameters and the overall performance of the NDN network. This study applies supervised learning to classify consumer-requested interests to overcome this issue. This technique leverages several related variables to accurately classify these interests. The outcomes of the conducted research demonstrated that raw data from the mini-NDN output can be processed and transformed into a usable dataset. This data is then utilized to train a classification model with supervised learning. In a scenario with 9 NDN nodes and varying numbers of interests, distributed both uniformly and according to Zipf's law, the Random Forest model performs effectively, achieving an accuracy rate of 86.2% with an error rate of 14.8%.
Co-Authors Adityo Nugroho Agustyna Diyah Sri Kamulyan Ahmad Marzuqi Ainun Nisa Alya Ghaitsa Rizky Pertiwi Anisa Herdiani Arie Ardiyanti Arie Ardiyanti Suryani Arief Nugroho, Muhammad Asri Inna Khoirun Nissa Atik Zilziana Muflihati Noor Aufa Eka Putri Lesatari Candra Widya Iswara Candra Widya Iswara, Candra Widya Dana Kusumo Dawam Dwi Jatmiko Suwawi Delvine Toleng Edyt Daryfayi Eka Himawanto Eko Darwiyanto Erricson Hardiansyah Faris Muhammad Firdaus Anggraini Gede Agung Ari Wisudiawan Gede Agung Ary Wisudawan Gregorius Pradana Satriawan Hafiyy Viazola Aziz I Putu Prima Ananda Ilham Akbar Indra Lukmana Sardi Jiddy Abdillah Jihan Ratnasari1 Joshua Chrisdiyanto Kemas Rahmat Saleh Wiharja Kiki Adhinugraha Kiki Adinugraha Kiki Maulana Adhinugraha Laksitowening, Kusuma Ayu Marinda Endi Lestari Markus Bernard1 Miftahul Adnan Rasyid Moch Arif Bijaksana Mohamad Fauzy Mohammad Fauzy Muhamad Hamda Muhammad Rezaldy Muhammad Thomy Farhan Murahartawaty Murahartawaty Paramita Mayadewi Pramudita Oktaviani Puput Fajriati Tri S Rachmadini P Utami Raginda Firdaus Rahmi Rohdiniyah Ramadhan Usman , Muchlis Ramadhyni Rifani Ratna Mayasari Rio Pratama Seno Anunggroho Shaufiah . Shinta Yulia Puspitasari Sri Astuti Sri Astuti Tody Ariefianto Wibowo Tora Fahrudin Yanuar Firdaus Arie Wibowo Yusza Reditya M