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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Onoma: Pendidikan, Bahasa, dan Sastra Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Rizwan Arisandi
Universitas Bina Nusantara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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Analysis of Pneumatic Systems Using the Laplace Equation Based on Python Programing Adhe Lingga Dewi; Rizwan Arisandi
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v4i2.16266

Abstract

Analysis for Pneumatic Systems using the Python programming language on Google Colab has been carried out. The steps taken before the simulation are to design a pneumatic system and then determine the Laplace equation based on Bernoulli's law equation. The Python programming language on Google Colab was chosen because it allows users to type Python code in a web browser and can be used for free. The simulation is carried out by varying the value of capacitance (C) from 50 kPa - 300 kPa with an increase of 50 kPa. This aims to determine the effect of changes in capacitance variations on the pressure exerted on the pneumatic system. Based on the simulation results, the greater the capacitance variation given, the resulting graph will be more sloping or closer to the x-axis. The C = 50 kPa variation has a steeper graph, while the C = 300 kPa variation has a more sloping graph and is closer to the x-axis.
Citra Pembelajaran Berbasis Teknologi di Perguruan Tinggi; Kendala dan Mutu Pembelajaran Online Mohammad Dika Raswadi; Puspita Kartikasari; Rizwan Arisandi; Ramly Ramly
Jurnal Onoma: Pendidikan, Bahasa, dan Sastra Vol. 11 No. 1 (2025)
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/onoma.v11i1.5451

Abstract

Perkembangan teknologi dan ilmu pengetahuan memberi kemudahan dalam menyelesaikan pekerjaan termasuk bidang pendidikan. Dalam era Revolusi Industri 4.0 penggunaan teknologi pada aspek pendidikan menggeser praktik pembelajaran konvensional ke berbasis teknologi. Platform online yang digunakan membuat pembelajaran lebih mudah dan fleksibel sambil dipersepsikan memiliki kendala dan penurunan mutu proses. Penelitian ini mengkonfirmasi temuan sebelumnya yang mencitrakan permasalahan dalam pembelajaran on line berlaku relatif sama pada semua kelompok sasaran penelitian tanpa mempertimbangkan stratifikasi dan klaster pengguna. Tujuan penelitian ini adalah mendeskripsikan citra pembelajaran dengan teknologi dalam pembelajaran online, mutu komunikasi timbal balik dosen-mahasiswa, kedisiplinan, dan aktivitas individu dalam belajar. Populasi penelitian mencakup seluruh mahasiswa perguruan tinggi negeri dan swasta di ibu kota Provinsi Sulawesi Selatan dan Sulawesi Barat, Indonesia. Sampel terdiri dari 255 mahasiswa yang dipilih secara acak. Data dikumpulkan dengan angket dan dianalisis secara deskriptif. Hasil penelitian mengungkap bahwa mahasiswa mempersepsi adanya kendala namun tidak signifikan saat pembelajaran. Mutu komunikasi timbal balik dosen-mahasiswa maupun antarmahasiswa relatif sama dengan saat pembelajaran tatap muka, belajar individual mahasiswa juga demikian, kedisiplinan menurun dibandingkan saat pembelajaran tatap muka. Perlu penanaman sikap positif dan kontrol perilaku mahasiswa agar pelaksanaan pembelajaran Online menjadi lebih optimal.
Combining depthwise separable convolution and transfer learning for pneumonia classification Puspita Kartikasari; Rukun Santoso; Suparti Suparti; Rizwan Arisandi; Vikri Haikal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4792-4805

Abstract

Pneumonia represents a prevalent pulmonary illness within the general population, significantly impacting diverse demographic groups, most notably infants. Therefore, identifying the disease at an early stage is essential to manage the condition effectively and boost the likelihood of a complete recovery via proper therapeutic interventions. This research endeavors to construct a robust early diagnostic framework based on medical imaging of pneumonia. The methodology introduced evaluates the synergy of depthwise separable convolution, transfer learning, and soft voting mechanisms, where each predictive model undergoes rigorous optimization via particle swarm optimization (PSO). For this study, the dataset is strictly categorized into two distinct groups: normal and pneumonia-infected. The analytical findings demonstrate that the PSO-optimized soft voting ensemble achieves a peak accuracy rate of 92.11%. Such a substantial accuracy level suggests that the developed framework can function as a dependable supplementary diagnostic instrument in clinical environments, which could minimize human diagnostic errors and substantially reduce the time radiologists spend screening critical cases. Moreover, embedding these computational methodologies into interactive, user-friendly web dashboard framework highlights the model’s practical feasibility for delivering real-time diagnostic assistance in resource-constrained environments.
Co-Authors Adhe Lingga Dewi Mohammad Dika Raswadi Puspita Kartikasari Ramly Ramly Rukun Santoso Suparti Suparti Vikri Haikal