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All Journal Jurnal Informatika dan Teknik Elektro Terapan International Journal Of Computer, Network Security and Information System (IJCONSIST) Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia
Alya Namira
Universitas Negeri Medan
Humanities Computer Science & IT Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Transportation

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Analisis Perbandingan Kinerja Binary Search Tree dan AVL Tree dalam Sistem Pencarian Data Mahasiswa Siti Haliza Zamili; Alya Namira; Khodotun Hadawiyah Margolang; Adinda Soleha; Adidtya Perdana
BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia Vol. 3 No. 6 (2026): BINER : Jurnal Ilmu Komputer, Teknik dan Multimedia
Publisher : CV. Shofanah Media Berkah

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

Abstract

Information retrieval systems are a crucial part of data management, particularly in academic systems that store a large amount of student data. Search effectiveness is greatly influenced by the format and organization of the data. This study aims to evaluate and compare the performance of two types of tree data structures, namely Binary Search Trees and AVL Trees, in student data retrieval activities. Testing was conducted using numerical datasets reflecting student information with varying amounts of data: 20, 40, 60, 80, and 100. Parameters used in the assessment included tree height and data search duration. The algorithm was implemented using the Python programming language. The test results show that Binary Search Trees tend to have tree heights that increase significantly with increasing data volume due to the absence of a balancing mechanism. Meanwhile, AVL Trees can maintain the balance of their tree structure through a rotation process that makes the tree height more consistent. In addition, search time in AVL Trees also appears faster than Binary Search Trees, especially when the amount of data encountered is larger. Therefore, AVL Trees are considered more efficient and ideal for implementation in student data retrieval systems that require fast and stable searches.
RANCANG BANGUN SISTEM DETEKSI TOXIC DAN SPAM BERBASIS FINITE AUTOMATA DENGAN ALGORITMA AHO-CORASICK Alya Namira; Zulfahmi Indra; Adinda Soleha; Bryant Tinambunan
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.9864

Abstract

Perkembangan media sosial meningkatkan risiko penyebaran konten toxic dan spam yang dapat menurunkan kualitas interaksi pengguna. Penelitian ini bertujuan merancang dan mengimplementasikan sistem deteksi konten toxic dan spam berbahasa Indonesia berbasis Finite Automata menggunakan algoritma Aho-Corasick. Dataset yang digunakan terdiri dari dataset_kata_kasar_idn sebanyak 3.111 data dan dataset_spam_idn sebanyak 2.636 data. Dari kedua dataset tersebut diekstrak 89 kata toxic dan 83 kata/frasa spam yang digunakan sebagai kamus keyword sistem. Sebelum proses deteksi, teks melalui tahap preprocessing berupa case folding dan normalisasi karakter berulang. Selanjutnya, keyword dimasukkan ke dalam struktur trie dan dibangun failure function untuk membentuk automata Aho-Corasick. Pengujian dilakukan terhadap 48 kalimat uji yang terdiri atas kategori Toxic, Spam, Normal, dan Toxic+Spam. Hasil penelitian menunjukkan bahwa sistem mampu mencapai akurasi sebesar 91,7% dengan nilai precision rata-rata 0,925, recall 0,939, dan F1-score 0,931. Hasil tersebut menunjukkan bahwa pendekatan Finite Automata dengan algoritma Aho-Corasick efektif digunakan untuk mendeteksi konten toxic dan spam secara cepat pada teks berbahasa Indonesia.
Implementation of a Telegram Bot for Router Monitoring Using Python and the RouterOS API Alya Namira; Dedy Kiswanto; Adinda Soleha
IJCONSIST JOURNALS Vol 8 No 1 (2026): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v8i1.206

Abstract

Network monitoring plays an important role in maintaining the stability and availability of computer network services. Manual monitoring approaches often cause delays in detecting network faults and responding to disruptions. This study implements a MikroTik router monitoring system that integrates Python, the RouterOS API, a Telegram Bot, and a Streamlit dashboard to support real-time fault detection and administrator notification. The system periodically checks router connectivity status and automatically detects when a router transitions between online and offline states. Upon detecting a status change, the system delivers immediate notifications to administrators through Telegram Bot and records the event in a log file for diagnostic purposes. Router metrics including CPU usage, memory availability, uptime, and interface conditions are displayed through a web-based interactive dashboard, providing administrators with a visual interface for monitoring network health. System testing was conducted by simulating router online and offline conditions. Results show that the system successfully detects router status changes, maintains diagnostic logs, and delivers real-time notifications via Telegram and a web-based monitoring interface. This integration of automated fault detection, mobile notification, and web-based visualization offers an effective approach to improving network monitoring responsiveness.
Co-Authors Adidtya Perdana, Adidtya Adinda Soleha Bryant Tinambunan Dedy Kiswanto Khodotun Hadawiyah Margolang Siti Haliza Zamili Zulfahmi Indra, Zulfahmi