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All Journal Techno.Com: Jurnal Teknologi Informasi JUITA : Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan JAIS (Journal of Applied Intelligent System) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal ICT : Information Communication & Technology Infotekmesin JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Innovation Information Technology and Application (JINITA) Brahmana : Jurnal Penerapan Kecerdasan Buatan JOINT (Journal of Information Technology Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) Jurnal Puan Indonesia Tedc Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Journal of Informatics and Electronics Engineering Journal Of Artificial Intelligence And Software Engineering Competitive Jurnal Informatika: Jurnal Pengembangan IT IPSIKOM Scientific Journal of Informatics Journal of New Trends in Sciences JPPkM: Jurnal Pengabdian dan Pemberdayaan kepada Masyarakat Journal of Applied Information Technology and Innovation
Castaka Agus Sugianto
Politeknik TEDC
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Edukasi Microsoft Word dalam Membuat Karya Ilmiah di SMP Negeri 10 Cimahi Ayu Hendrati Rahayu; Firdhani Faujiyah; Castaka Agus Sugianto; Gerinata Ginting; Dini Rohmayani; Ari Sudrajat; Taufik Fajar Mustafa
JPPkM: Jurnal Pengabdian dan Pemberdayaan kepada Masyarakat Vol. 1 No. 2 (2025): JPPkM:Juli
Publisher : Yayasan Pemimpin Inovasi Science

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This Community Service Program (PKM) aims to improve the skills of students at SMP Negeri 10 Cimahi in using Microsoft Word for academic writing purposes. According to reports from the school, many students face difficulties in understanding the functions and usage of word processing software, particularly in composing scientific papers. The activity was conducted over three days through hands-on practical sessions in the school’s computer laboratory, utilizing pre-prepared academic writing templates. The results showed significant improvement in students' technical abilities, although several challenges were encountered, such as lack of basic knowledge about computer hardware and the use of outdated versions of Microsoft Word. The program not only enhanced students' practical skills but also fostered their interest in technology and boosted their confidence in completing academic assignments. It is hoped that similar programs can be implemented more widely to support technology-based learning at the basic education level. 
Pelatihan Pemanfaatan Smartphone untuk Digital Marketing di SMK Negeri 3 Cimahi Dini Rohmayani; Arya Aditya; Castaka Agus Sugianto; Ayu Hendrati Rahayu; Aris Haris Rismayana
JPPkM: Jurnal Pengabdian dan Pemberdayaan kepada Masyarakat Vol. 2 No. 1 (2026): JPPkM:Januari
Publisher : Yayasan Pemimpin Inovasi Science

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This community service activity aims to provide training on utilizing smartphones for digital marketing to 70 students of SMK Negeri 3 Cimahi through introducing basic concepts, Shopee features, and practical sessions on creating online stores. The training was conducted on August 23, 2022, using methods of counseling, demonstration, and hands-on practice with participants' smartphones. Results showed high enthusiasm among participants, who successfully understood the material and created their own Shopee stores, thereby enhancing digital entrepreneurship skills in the industry 4.0 era.
Algoritma Naive Bayes untuk Klasifikasi Ketepatan Waktu Kelulusan Mahasiswa Politeknik TEDC Bandung Nandhita Ayusari; Castaka Agus Sugianto
Journal of Applied Information Technology and Innovation Vol. 1 No. 1 (2025): Maret
Publisher : Yayasan Pemimpin Inovasi Science

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Polytechnic TEDC Bandung  is a higher education institution that is committed to increasing efficiency and effectiveness in the education and teaching process by implementing various policies and programs. One of the aims of this is to produce quality student graduates who are useful for society. One of the steps that students need to take to become quality graduates is to graduate on time. However, in its implementation, there are still some students who experience obstacles in achieving this. This is caused by several factors, so efforts are needed to reduce or even overcome this. This research aims to apply the Naive Bayes algorithm to be able to classify student data that is on time and not on time when attending the final assignment session, in order to obtain solutions and efforts that can help the campus to overcome this problem. The test results using the naïve Bayes method without validation produced 218 data that were included in the on-time class and 33 data that were included in the not-on-time class. Meanwhile, the results of testing using the naïve Bayes method using validation produced 216 data that were included in the on-time class and 35 data that were included in the not-on-time class. Test results using the Naïve Bayes method using validation with RapidMiner produced an accuracy level of 92.05%, precision had a value of 92.40%, Recall had a value of 98.52% and F1-Score had a value of 95.71%.
Aplikasi Penjualan Berbasis Web (Studi Kasus Kedai “The Susumurni Inc “) Dini Rohmayani; Castaka Agus Sugianto; Novita Lestari Anggreini; Aqmal Mulqy Bagja Laksana
Journal of Applied Information Technology and Innovation Vol. 1 No. 2 (2025): September
Publisher : Yayasan Pemimpin Inovasi Science

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The Susumurni Inc merupakan perusahaan yang bergerak dalam bidang minuman dengan menu yang disajikan ialah susu murni. Hasil observasi pada kedai the susumurni inc dari segi sulitnya merekap data dan buku menu yang terkadang menunggu untuk menggunakannya. Beberapa transaksi penjualan yang tidak diketahui serta jumlah total pendapatan penjualan yang tidak diketahui keseluruhan nominalnya, maka penulis melakukan penelitian di kedai susu murni yang dapat membantu pemilik usaha dalam mengetahui perkembangan pendapatan pada usahanya, serta membantu mempercepat waktu dalam melakukan pencatatannya, maka dibuatlah aplikasi berbasis web. Metode yang diimplementasikan yaitu metode waterfall dengan tahapan analisis, desain, pengodean, pengujian. Menggunakan pemodelan berorientasi objek menggunakan Unified Modeling Language (UML). Aplikasi The Susumurni Inc berbasis web dan dibangun menggunakan bahasa pemrograman PHP, CSS, JavaScript, framework Laravel dan database MySQL. Aplikasi UMKM Studi Kasus The Susumurni Inc Berbasis Web dapat membantu pemilik dalam mengatur sistem perusahaan untuk merekap data serta memudahkan dalam persediaan menu yang bisa diakses oleh semua pelanggan tanpa menunggu antrian. Hasil uji Black Box telah mengindikasikan bahwa fitur dalam sistem telah berjalan sesuai dengan yang diharapkan. Hasil User Acceptance Test (UAT) berdasarkan 3 parameter uji dengan presentasenya yaitu desain (96,00%), fitur (94.67) dan kepuasan (92,89%) memperoleh skor rata-rata keseluruhan (88,61%).
Algoritma C4.5 Untuk Klasifikasi Penerima Bantuan Covid-19 Pada Desa Cimareme, Bandung Barat Castaka Agus Sugianto; Muhammad Ridwan; Dini Rohmayani; Novita Lestari Anggreini; Ayu Hendrati Rahayu
Journal of Applied Information Technology and Innovation Vol. 1 No. 2 (2025): September
Publisher : Yayasan Pemimpin Inovasi Science

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Desa Cimareme yang terletak di Kabupaten Bandung Barat merupakan salah satu desa penerima bantuan pemerintah. Namun, beberapa warga mengeluhkan ketidakadilan dalam pendistribusian bantuan, di mana ada warga yang dianggap mampu justru menerima bantuan, sedangkan yang membutuhkan tidak mendapatkannya. Untuk menghindari kesalahan sasaran, diperlukan pengklasifikasian data yang dilakukan secara ilmiah dan sistematis guna menentukan siapa saja yang berhak menerima bantuan dan siapa yang tidak. Berdasarkan hal tersebut, peneliti melakukan pengolahan data menggunakan metode data mining untuk mengklasifikasikan penerima dan bukan penerima bantuan COVID-19 dengan menggunakan Algoritma Decision Tree dan Algoritma Naïve Bayes sebagai pembanding. Tujuannya adalah untuk menemukan pola dalam program bantuan pemerintah COVID-19 serta mengetahui tingkat akurasi Algoritma Decision Tree (C4.5)  jika dibandingkan dengan algoritma lainnya. Penelitian ini menggunakan data kependudukan dari Desa Cimareme, Kecamatan Ngamprah, Kabupaten Bandung Barat. Model data mining dikembangkan menggunakan perangkat RapidMiner. Berdasarkan hasil pengujian dan validasi, Algoritma Decision Tree menghasilkan akurasi sebesar 99,97%, precision 100,00%, recall 99,71%, dan nilai AUC sebesar 0,967. Sedangkan Algoritma Naïve Bayes menghasilkan akurasi 99,93%, precision 99,71%, recall 99,71%, dan AUC sebesar 0,997. Hasil uji T-test menunjukkan nilai alpha sebesar 0,643, yang berarti tidak terdapat perbedaan signifikan antara hasil Algoritma Decision Tree dan Naïve Bayes dalam klasifikasi penerima bantuan.
ANALISIS SENTIMEN MASYARAKAT TERHADAP PINJAMAN ONLINE DI APLIKASI X MENGGUNAKAN LONG SHORT-TERM MEMORY Hafizh Maalik Falah; Castaka Agus Sugianto
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.429

Abstract

The development of online loans in Indonesia has led to various public opinions spread across social media, one of which is the X platform. This research aims to analyze public sentiment towards online loans using the Long Short-Term Memory (LSTM) method. The data used consists of 702 Indonesian tweets collected through a crawling process with Tweet Harvest. Of these, 480 tweets were classified as positive sentiment and 222 as negative. The research process includes preprocessing, manual labeling, model training, and evaluation stages. The model was built using Sequential architecture from Keras, consisting of embedding layer, LSTM layer 128 units, 30% dropout, and output layer with softmax activation function. The model was trained using 562 tweets as training data and 140 tweets as validation data with a ratio of 80:20, for 10 epochs and batch size 64. The final evaluation using the entire dataset resulted in 92.59% accuracy, with 79.06% precision, 79.43% recall, and 79.14% F1-score. These results show that LSTM is able to classify sentiment stably and effectively, and has strong potential in sentiment analysis on short text data such as tweets.
DETEKSI KESEGARAN IKAN BANDENG DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) Rudi Riansyah; Castaka Agus Sugianto
IPSIKOM Vol. 13 No. 2 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i2.442

Abstract

Fish freshness is a key indicator in ensuring food quality and safety, especially in milkfish (Chanos chanos) which is widely consumed in Indonesia. Manual freshness assessment is subjective and requires special skills, so an accurate automated approach is needed. This study aims to develop a digital image-based milkfish freshness classification application using the Convolutional Neural Network (CNN) method with a transfer learning approach. The dataset used consists of 445 milkfish images in two classes: fresh and not fresh, with an augmentation process to enrich the visual variety. Two models were compared: Model A (baseline) and Model B (enhancement with Dropout and fine-tuning). The evaluation results show that Model A has 33% accuracy, 50% precision, and 50% recall, In contrast, Model B has 67% accuracy, 50% precision, and 100% recall, showing more stable prediction in Streamlit-based applications. These findings suggest that the integration of CNN and transfer learning can be effectively applied to support the digitization of fish-based food product quality. Further development is suggested through the addition of training data, multi-class classification, and integration to mobile or IoT devices.
Co-Authors Ade Yuliana Aditia Gusman Aldy Noer Tjahyo Apnena, Riri Damayanti Aqmal Mulqy Bagja Laksana Ari Sudrajat Aris Haris Rismayana Arya Aditya Ayu Hendrati Rahayu Candra Irawan Christy Atika Sari Dadan Saepul Ramdan Dahlan Dahlan Dini Rohmayani Eko Hari Rachmawanto Faishal Fachruddin Febianto, Aldi Kristiawan Firdhani Faujiyah Fitrah, La Ode Muhammad Fredricka, Jhoanne Gerinata Ginting Hafizh Maalik Falah Heldi Akbar Kosasih Hidayah, Hamimah Nur Himawan, Aldi Iqna Aulia Iskandar, Rachmat Juniardi, Trianda Kosasih, Heldi Akbar Lela Kesmasari Maulana, Firdi Rizky Melani Nur Astita Mohamed Doheir Muhammad Ridwan Nadya Selvi Pujiyanita Nafea, Mohammed Mansoor Nandhita Ayusari Nia Ekawati, Nia Novita Lestari Anggreini Pebriana, Sela Perdana, Rangga Satria Pujiyanita, Nadya Selvi Rahmawan, Septiadi Ramdan, Dadan Saepul Riansyah Ramadhan, Gery Riyan Riyan Rizqy Dimas Monica Rosdianti, Elfi Rudi Riansyah Safira, Rifa Sari, Putri Nurlaela Shandy Tresnawati Sukmawati, Dini Syaefuloh, Herman Syams, Fawwaz Muhammad Syifa Fauzia Tahir Tahir, Syifa Fauzia Tati Ernawati Taufik Fajar Mustafa Tresnawati, Shandy Tri Herdiawan Apandi Tri Pratiwi Olivia Riska Bokings Wanaziana, Keny Kirana Zulkipli ., Zulkipli Zundi, Teguh Muhammad