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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) IPSIKOM Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Nurasiah
Universitas Insan Pembangunan Indonesia
Computer Science & IT Control & Systems Engineering

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SISTEM MANAJEMEN INVENTORY GUDANG BERBASIS WEBISTE DENGAN METODE PROTOTYPE Cornelius Candra Lase; Falentina Ike Oktavia; Indra Mahendra Munthe; Nur Santiya; Randi Pitra; Winanti Winanti; Jumiran Jumiran; Sucipto Basuki; Nurasiah; Beby Tiara
IPSIKOM Vol. 13 No. 1 (2025): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v13i1.413

Abstract

Gudang PT. XYZ selama ini dalam pengelolaan inventarisasi gudang masih menggunaan sistem konvensional dan sebagian telah menggunakan aplikasi excel untuk pendataan barang. Namun masih banyak kelemahan terutama sistem konvensional yang membutuhkan waktu lama dalam pencarian data dan pembuatan laporan belum bisa dilakukan secara realtime. Perlu adanya sistem manajemen inventory gudang untuk mengatasi permasalahan tersebut. Metode pengembangan sistem menggunakan metode prototype dan pengujian dilakukan dengan black box testing. Sistem manajemen inventory dibangun berbasis website untuk saat ini. Sistem dibangun dan dirancang menggunakan Unifield Modelling Language (UML) dengan bahasa pemograman PHP dan database MySQL. Sistem telah melewati uji coba atau testing sehingga sistem dapat diimplementasikan secara maksimal tanpa kendala. Kedepannya akan dikembangkan sistem lebih lanjut dengan sistem manajemen inventory berbasis mobile yang disesuaikan dengan fitur-fitur yang lebih baik lagi.
PERANCANGAN SISTEM INFORMASI BANK SAMPAH (ASRIGO) BERBASIS WEB PADA PERUMAHAN SERDANG ASRI 2 PANONGAN Nana Supiana; Nurasiah; Yayah Yulia; Bonar Napitupulu; Didi Sutardi; Samsul Makin; Joni Iskandar; Ferdi Kuswandi; Karnawi Kamar; Gusli Chidir; Adiyanto; Firdaus Putra; Hatoli Waruwu; Adrimal; Azhari; Yusuf Sudiono; M rizki Wijaya; Akhmad Farhan; Yeremia Mendrofa
IPSIKOM Vol. 14 No. 1 (2026): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v14i1.444

Abstract

Pengelolaan bank sampah merupakan salah satu upaya strategis dalam mengatasi permasalahan lingkungan sekaligus mendukung penguatan ekonomi masyarakat. Bank sampah merupakan salah satu strategi penerapan 3R (Reuse, Reduce, Recycle) dalam pengelolaan sampah pada sumbernya di tingkat masyarakat dengan pola insentif ekonomi. Penelitian ini bertujuan untuk memberikan manfaat kepada masyarakat sekitar melalui program edukasi dan layanan yang telah disiapkan, mengkaji implementasi digitalisasi pengelolaan bank sampah melalui Sistem AsriGO dan dampaknya terhadap peningkatan kapasitas masyarakat melalui pelatihan serta pemanfaatan teknologi digital. Metode penelitian menggunakan pendekatan kualitatif dengan studi kasus di wilayah Perumahan Serdang Asri 2 yang akan menggunakan sistem AsriGO. Hasil penelitian menunjukkan bahwa digitalisasi dengan AsriGO mempermudah proses pencatatan, penilaian dan transaksi sampah, serta meningkatkan partisipasi dan pendapatan anggota. Pelatihan yang terintegrasi dalam sistem membantu masyarakat dalam pengelolaan sampah yang lebih efektif, sehingga berkontribusi pada penguatan ekonomi lokal. Penelitian ini merekomendasikan perluasan pelatihan dan peningkatan fitur AsriGO untuk mendukung keberlanjutan pengelolaan bank sampah secara Digital
ANALISIS KEAMANAN APLIKASI WEB MENGGUNAKAN METODE PENETRATION TESTING BERDASARKAN FRAMEWORK ISSAF PADA APLIKASI WEB MILIK PEMERINTAH DAERAH JAWA TIMUR Budi Haryanto; Alfiano Alfattah; Adiyanto; Nurasiah
IPSIKOM Vol. 14 No. 1 (2026): Jurnal Ipsikom
Publisher : LPPM UNIVERSITAS INSAN PEMBANGUNAN INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58217/ipsikom.v14i1.453

Abstract

Digitalisasi layanan public telah mendorong banyak instansi pemerintah daerah di Indonesia untuk menghadirkan aplikasi berbasis web. Namun, tantangan keamanan siber sering kali tidak diantisipasi dengan memadai.Penelitian ini menganalisis kerentanan keamanan pada salah satu aplikasi web milik pemerintah daerah menggunakan metode penetration testing berbasis Information System Security Assessment Framework (ISSAF). Fokus analisis mencakup tiga cektor utama: Improper Input Handling, Information Disclosure, Denial of Service (DoS). Pengujian ini dilakukan melalui simulasi eksploitasi menggunakan payload karakter Unicode yang dikirimkan melalui parameter URL. Hasil menunjukkan bahwa system tidak melakukan validasi input secara ketat, menampilkan stack trace error secara public, dan rentan terhadap DoS aplikasi. Rekomendasi perbaikan mencakup penerapat validasi input, normalisasi UT-8, konfigurasi error handling yang aman, serta implementasi Web Application Firewall (WAF). Penelitian ini diharapkan dapat menjadi referensi bagi instansi pemerintah dalam meningkatkan ketahanan siber informasi public.
SENTIMENT ANALYSIS OF TWITTER (X) RESPONSES USING NAÏVE BAYES: A CASE STUDY OF JUMBO: SENTIMENT ANALYSIS Nurasiah; Latifah Nur Zam Zami; Yunianto Agung Nugroho
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8037

Abstract

The rapid growth of social media platforms, especially Twitter (now rebranded as X), has made it a rich source for public opinion mining. This study focuses on analyzing public sentiment toward the animated “film Jumbo" by applying the Naïve Bayes Classifier algorithm to Twitter data. The research involves collecting tweets related to the film, preprocessing the data by removing noise such as stopwords, user mentions, and special characters, and then classifying sentiments into positive, negative, and neutral categories. The Naïve Bayes algorithm was chosen due to its effectiveness and efficiency in text classification tasks such as sentiment analysis. The classifier is trained on a labeled dataset and evaluated through performance metrics including accuracy, precision, recall, and F1-score. The results demonstrate that the Naïve Bayes Classifier can effectively capture public sentiment on social media, providing insights into audience reception of the “film Jumbo." Moreover, this research highlights the importance of integrating machine learning techniques with social media analytics for real-time sentiment monitoring. The model achieved an accuracy of 77% in classifying sentiments. Analysis results indicate that positive sentiment dominates public opinion, with a precision of 88% and recall of 83%, followed by negative sentiment with precision and recall of 75% and 71%, respectively, and neutral sentiment with precision of 40% and recall of 57%. These findings offer valuable insights for the film industry to better understand audience perceptions and make informed decisions.
A Systematic Literature Review: Impact of Generative AI as Technology to Learning in Higher Education Winanti Suprobo; Sucipto Basuki; Nurasiah
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 2 (2024): Oktober 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i2.3056

Abstract

The development of generative Artificial Intelligence (AI) technology cannot be avoided because developments have entered all domains of life, including the world of education. Universities are preparing themselves to implement generative AI to support learning to be more efficient and effective without having to fear the threats it poses. The method used in this paper is a systematic literature review by exploring existing research through predetermined keywords. Extraction and selection of previous research results that are relevant to the topic are carried out and irrelevant topics are removed from the discussion. The papers produced from previous studies used as sources for the systematic literature review were 28 selected study papers, with range from 2004 to 2023. The most papers published in 2023 being 23 papers. The authors of the papers used were 24 authors from academic background, while the rest from industry and general publics. Generative AI, especially ChatGPT usage capabilities of learning in higher education, can create sophisticated text as humans do, as a virtual tutor and answer questions quickly, edit videos, help with manufacturing design, and act as language generation, language translation, and summarization. The advantages of Generative AI as a choice that cannot be underestimated and avoided and can be a responsive friend with accurate judgment. In order not to harm students, there needs to be strict and clear regulations so that the use of generative AI does not harm students, lecturers, and universities. Generative AI also has negative impacts, including decreasing knowledge productivity, ethics, and law becoming challenges, cheating, and hampering skill development, producing biased information, and low levels of privacy. In the future, universities should prepare clear regulations and policies for data security and privacy so that data remains safe and generative use has a positive and beneficial impact on higher education.
Co-Authors Adiyanto Adrimal akhmad farhan Alfiano Alfattah Azhari Beby Tiara Bonar Napitupulu Budi Haryanto Cornelius Candra Lase Didi Sutardi Falentina Ike Oktavia Ferdi Kuswandi Firdaus Putra Gusli Chidir Hatoli Waruwu Indra Mahendra Munthe Joni Iskandar Jumiran Jumiran Karnawi Kamar Latifah Nur Zam Zami Muhamad Rizky Wijaya Nana Supiana Nur Santiya Randi Pitra Samsul Makin Sucipto Basuki Winanti Suprobo Winanti Winanti Yayah Yulia Yeremia Mendrofa Yunianto Agung Nugroho Yusuf Sudiono