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All Journal Jurnal Ilmiah Informatika Komputer Techno.Com: Jurnal Teknologi Informasi Jurnal Informatika dan Teknik Elektro Terapan JAIS (Journal of Applied Intelligent System) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Batoboh : Jurnal Pengabdian Pada Masyarakat JURIKOM (Jurnal Riset Komputer) Jurnal Manajemen Informatika Jurnal Informatika Kaputama (JIK) Jurnal Kridatama Sains dan Teknologi Jurnal Manajemen Informatika dan Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer & Kecerdasan Buatan Parameter Journal of Innovation Information Technology and Application (JINITA) BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi dan Sistem Komputer Jurnal Teknik Informatika (JUTIF) Jurnal AbdiMas Nusa Mandiri JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) Jurnal Pengabdian Teknologi Tepat Guna Jurnal Difusi Jurnal Pengabdian Masyarakat IPTEK Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) TRANSFORMASI : JURNAL PENGABDIAN PADA MASYARAKAT Journal of Economic, Business and Engineering (JEBE) Jurnal Pengabdian kepada Masyarakat Desa (JPMD) Journal of Computer Science and Informatics Engineering Toba: Journal of Tourism, Hospitality, and Destination Dulang: Jurnal Pengabdian Kepada Masyarakat PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sains Informatika Terapan (JSIT) Jurnal Pengabdian Masyarakat Fakultas Teknik: Jurnal Abditek Abdi Makarti Jurnal Pengabdian Masyarakat Bhinneka SAINSTECH: Jurnal Penelitian dan Pengkajian Sains dan Teknologi Dharma Pengabdian Perguruan Tinggi (DEPATI) Journal of Artificial Intelligence and Digital Business Mestaka: Jurnal Pengabdian Kepada Masyarakat Innovative: Journal Of Social Science Research TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Journal of Data Science Theory and Application Tabsyir: Jurnal Dakwah Dan Sosial Humaniora AL-KARIM: Journal of Islamic and Educational Research Jurnal Pengabdian Kepada Masyarakat (JPKM) Al-Nizam: Indonesian Journal of Research and Community Service Jurnal Intelek Dan Cendikiawan Nusantara EDUJAVARE: International Journal of Educational Research Jurnal Tarreang: Tren Pengabdian Masyarakat Agrokompleks Inovasi Gagasan Abdimas & Kuliah Kerja Nyata WISDOM : Jurnal Pengabdian Kepada Masyarakat Wisdom LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Fundamentum: Jurnal Pengabdian Multidisiplin Academic Journal of Islamic Principles and Philosophy Jurnal Pengabdiaan Masyarakat Larisma Journal of Community Research & Engagement (JCRE) Jipmas : Journal Inovasi Pengabdian Masyarakat Jurnal Lentera: Penelitian dan Pengabdian Masyarakat DCS: Jurnal Pengabdian Masyarakat Man-Ana Smart Humanity: Jurnal Pengabdian Masyarakat JES-TM Social and Community Service Jurnal Teknologi Pendidikan Jurnal Pengabdian kepada Masyarakat (DIASYA) Kolaborasi Masyarakat NJCOM: Community Service Journal Journal of Applied Community Service and Innovation BHAKTI Bhinneka Al-Madrasah: Jurnal Pendidikan, Pembelajaran dan Kebudayaan Jurnal Pengabdian IPTEK IKHTIAR: Jurnal Pengabdian Kepada Masyarakat Abdimas Altruis: Jurnal Pengabdian kepada Masyarakat
Imam Prayogo Pujiono
UIN K.H. Abdurrahman Wahid Pekalongan
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PENERAPAN WEBSITE BLOGGER SEBAGAI INOVASI PEMBELAJARAN DIGITAL UNTUK MENINGKATKAN KOMPETENSI MENULIS SISWA SMP NEGERI 14 PEKALONGAN kamilah, Arina Kharisatul; Eliza, Naila Nur; Alawiyah, Vina Vitri; Syahda, Talitha Ulima; Pujiono, Imam Prayogo
Al-Nizam: Indonesian Journal of Research and Community Service Vol. 4 No. 1 (2026): Al-Nizam: Indonesian Journal of Research and Community Service
Publisher : Muntaha Noor Institute

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

Abstract

The rapid development of digital technology has encouraged Indonesian language learning, particularly writing skills, to integrate technology-based instructional media. However, the use of digital media in teaching writing at the junior high school level remains limited. This study aims to examine the implementation of Blogger as a digital learning innovation to improve students’ writing competence at SMP Negeri 14 Pekalongan. This research employed a descriptive qualitative approach, with data collected through classroom observations, documentation of students’ writing published on Blogger, and reflective notes from teachers and researchers. The research subjects were eighth-grade students selected through purposive sampling. The findings indicate that the use of Blogger enhances students’ interest, engagement, and writing competence through online writing, revision, and publication activities. Students became more active, reflective, and confident in expressing ideas in written form. Nevertheless, several technical and non-technical challenges were identified, including limited internet access and varying levels of digital literacy. Overall, Blogger is proven to be an effective digital learning medium for writing instruction, supporting digital literacy development and 21st-century skills among junior high school students.
Analisis Perbandingan Struktur Data Dan Kompleksitas Koding Antara MySQL Dan MongoDB Pada Pengembangan Aplikasi Blog Ahmad Turmudi; Furqon Lanang; Muhammad Risqi Agung Pratama; Fuad Naufal Ihsan; Dicky Anggriawan Nugroho; Imam Prayogo Pujiono
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.916

Abstract

The dominance of Relational Database Management Systems (RDBMS) often introduces coding inefficiencies when handling hierarchical data structures, such as commenting features in modern web development. This study compares data structure design efficiency and coding complexity between MySQL and MongoDB within the context of blog application development. Employing a comparative experimental approach, the research simulates a Node.js-based article management module using a hybrid dataset that combines structured user profiles with dynamic volumes of nested comments. The analysis reveals significant architectural distinctions: MySQL necessitates strict normalization across five physical tables and complex join operations, whereas MongoDB leverages an embedded document model that eliminates the need for multi-table relations. Quantitatively, MongoDB demonstrated faster average read execution times (7.9 ms) compared to MySQL (10.7 ms) and yielded JSON data structures directly compatible with application objects, effectively resolving impedance mismatch issues. The study concludes that MongoDB offers superior developer productivity for use cases involving nested data, while MySQL remains the recommended choice for systems prioritizing strict referential integrity validation.
Pemodelan dan Prediksi Pengeluaran Bulanan Mahasiswa Berbasis Algoritma Ordinary Least Squares dalam Bahasa Pemrograman C++ Alisha Zulfa Salsabila; Talitha Falakh Tasya; Lina Azaria; Ilham Dwijokangko; Imam Prayogo Pujiono
Jurnal Kridatama Sains dan Teknologi Vol 8 No 01 (2026): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v8i01.2356

Abstract

Financial management is a common challenge faced by students living away from home, especially those who often run out of pocket money before the end of the month. This condition is not always caused by consumptive behavior, but also due to a lack of planning and a structured expenditure prediction system. This study developed a financial analysis program based on the C++ programming language with an object-oriented programming (OOP) approach that implements the Ordinary Least Squares (OLS) algorithm to model and predict students' monthly expenses based on the amount of pocket money they receive. The research data were obtained from 30 students with pocket money ranging from Rp 1,100,000 to Rp 3,500,000 per month, which reflects the real conditions of students in medium-large cities in Indonesia. Descriptive statistical analysis shows that on average, students spend approximately 82.9% of their pocket money each month. The modeling results show a regression equation Ŷ = Rp 119,867 + 0.8221 × X with a correlation coefficient of r = 0.9979 and a determination coefficient of R² = 0.9957, meaning that the pocket money variable is able to explain 99.57% of the variation in student expenditure. The F-test produces F-calculated = 6,732.45, which far exceeds F-table = 4.20 at a significance level of 5%, proving the model is statistically significant. Accuracy evaluation using MAE produces a value of Rp 18,142 (approximately 1% of the average expenditure) and an RMSE of Rp 23,457. It was also found that the marginal propensity to consume (MPC) phenomenon decreases with increasing pocket money, consistent with the economic theory of consumption. This study proves that the OLS algorithm implemented in C++ is an effective, accurate, and efficient tool for analyzing and predicting student financial patterns computationally.
EdTech Startups and Their Impact on Traditional Learning Models Imam Prayogo Pujiono
EDUJAVARE: International Journal of Educational Research Vol. 3 No. 1 (2025): EDUJAVARE: International Journal of Educational Research
Publisher : CV. Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/edujavare.v3i1.802

Abstract

The rapid growth of EdTech startups has introduced innovative tools that challenge traditional learning models, offering personalized, flexible, and technology-enhanced educational experiences. However, integrating these technologies into established educational institutions remains a complex and gradual process. This study aims to explore the impact of EdTech startups on traditional learning models, examining how these technologies disrupt, complement, or reinforce traditional pedagogical practices. Using a qualitative research design, the study employed semi-structured interviews, focus group discussions, and document analysis to gather data from educators, students, EdTech entrepreneurs, and administrators across urban centers in Indonesia. The findings reveal that while EdTech startups have the potential to enhance student engagement and learning personalization, their integration into traditional institutions is often hindered by infrastructural challenges, resistance to change, and a lack of digital literacy. The study highlights the role of the COVID-19 pandemic as a catalyst for accelerating EdTech adoption. In conclusion, the research underscores the need for a balanced approach that supports the professional development of educators, addresses equity in access to technology, and fosters collaboration between EdTech startups and traditional institutions. This study contributes to the growing literature on educational innovation, providing valuable insights for policymakers, educators, and EdTech developers seeking to integrate technology into learning environments effectively. 
Penguatan Literasi Iman Keluarga melalui Pengajian Tematik ‘Segenggam Iman’ bagi Jamaah Masjid Al-Hikmah Pekalongan Arditya Prayogi; M. Adin Setyawan; Imam Prayogo Pujiono; Riki Nasrullah
Journal of Applied Community Service and Innovation Vol. 2 No. 01 (2026): ISSUE JUNI
Publisher : PT. Mifandi Mandiri Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63401/jacsi.v2i01.896

Abstract

Penguatan pemahaman keagamaan masyarakat menjadi kebutuhan penting di tengah tantangan pengasuhan keluarga pada era digital. Kegiatan pengabdian kepada masyarakat ini bertujuan menguatkan pemahaman jamaah tentang iman sebagai fondasi pendidikan anak melalui pengajian tematik “Segenggam Iman untuk Anak Kita” di Masjid Al Hikmah Podosugih, Kota Pekalongan. Kegiatan melibatkan jamaah dan menggunakan metode service learning melalui tahapan perencanaan, pelaksanaan, dan evaluasi, dengan pendekatan mau‘izhah hasanah berupa ceramah tematik, dialog interaktif, dan refleksi kegiatan. Hasil kegiatan menunjukkan perubahan pengetahuan dan sikap peserta yang paling menonjol pada pemahaman bahwa pendidikan iman anak tidak cukup melalui nasihat, tetapi perlu dibangun melalui keteladanan, pembiasaan ibadah, komunikasi keluarga, dan pengawasan pergaulan. Indikator keberhasilan terlihat dari kehadiran 40 peserta/jamaah, partisipasi aktif dalam tanya jawab, serta kemampuan peserta merumuskan kembali peran orang tua sebagai teladan iman dalam keluarga. Perubahan ini tampak melalui respons peserta yang lebih reflektif saat mengaitkan materi dengan persoalan pengasuhan harian. Temuan ini menegaskan bahwa pengajian tematik berbasis masjid relevan sebagai model pengabdian yang partisipatif untuk memperkuat literasi keagamaan dan kesadaran religius masyarakat secara berkelanjutan.
Robust DeBERTa-v3 Framework for Aspect-Based Sentiment Analysis with Extreme Class Imbalance Muhammad Rikzam Kamal; Sabrina Ahmad; Rohmad Abidin; Imam Prayogo Pujiono
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3296

Abstract

Aspect-Based Sentiment Analysis (ABSA) often operates under extreme class imbalance, causing Transformer-based models to favour majority classes while failing to detect minority sentiments disproportionately. This study proposes RoABSA, a robust DeBERTa-v3–based framework that integrates Hybrid Semantic Augmentation, combining Easy Data Augmentation and Back-Translation, with cost-sensitive optimization via Weighted Random Sampling and Focal Loss to enhance diversity representation and recalibrate gradient contributions. Evaluated on four SemEval benchmarks (Lap14, Res14, Res15, Res16), RoABSA achieves consistent state-of-the-art performance with Macro-F1 scores of 84.71, 91.65, 88.40, and 84.24, respectively, outperforming strong graph-based and Transformer-based baselines by margins of up to 10.35 points. Ablation results confirm that robustness emerges from the synergy between augmentation and cost-sensitive learning rather than any single component. At the same time, per-class analysis demonstrates substantial gains in detecting minority sentiments, including cases where baselines failed. These findings highlight that addressing long-tailed distributions in ABSA requires coordinated interventions at both data and optimization levels, and that RoABSA provides a practical, generalizable strategy for improving stability and fairness in sentiment classification.
ANALISIS PERBANDINGAN EFISIENSI ALGORITMA INTROSORT DENGAN ALGORITMA TRADISIONAL BUBBLE SORT DAN SELECTION SORT Salsabila Amanda; Lilina Dwi Anastasya; Fira Sulistia; Nafisa Purnamasari; Imam Prayogo Pujiono
JEIS: Jurnal Elektro dan Informatika Swadharma Vol 6, No 1 (2026): JEIS EDISI JANUARI 2026
Publisher : Institut Teknologi dan Bisnis Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jeis.vol6no1.1086

Abstract

Data sorting is a fundamental operation that significantly impacts the performance of various computing applications. In C++, developers can choose from traditional sorting algorithms, such as Bubble sort and Selection sort, as well as modern algorithms, such as Introsort. This study aims to analyze and compare the efficiency of the Introsort, Bubble sort, and Selection sort algorithms in terms of execution time and memory usage. The method used is a comparative experiment with three random data sizes: 100, 1,000, and 10,000 elements. Each scenario is tested three times using a recursive C++ implementation on a Lenovo Ideapad 1i laptop with an Intel Celeron N4020 processor, 8 GB of RAM, and a 477 GB SSD. Execution time is measured using the std::chrono library, while memory usage is measured using the getMemoryUsage() function. The results show that Introsort is consistently the fastest algorithm for all data sizes. At 10,000 elements, the average execution time of Introsort is about 2.88 ms, while Bubble sort and Selection sort are about 1,303 ms and 1,067 ms, respectively. Thus, Introsort is about 450 times faster than Bubble sort and 370 times faster than Selection sort on large datasets, while the difference in memory usage among the three algorithms is under 0.15 MB. These findings indicate that choosing a modern algorithm like Introsort is highly recommended for sorting large data sets in C++. In contrast, traditional algorithms are more appropriate for learning scenarios or small datasets.Pengurutan data merupakan operasi dasar yang sangat memengaruhi kinerja berbagai aplikasi komputasi. Pada bahasa pemrograman C++, pengembang dapat memilih algoritma pengurutan tradisional seperti Bubble sort dan Selection sort, maupun algoritma modern seperti Introsort. Penelitian ini bertujuan menganalisis dan membandingkan efisiensi algoritma Introsort, Bubble sort, dan Selection sort berdasarkan waktu eksekusi dan penggunaan memori. Metode yang digunakan adalah eksperimen komparatif dengan tiga skala ukuran data acak, yaitu 100, 1.000, dan 10.000 elemen. Setiap skenario diuji sebanyak tiga kali menggunakan implementasi rekursif C++ pada laptop Lenovo Ideapad 1i dengan prosesor Intel Celeron N4020, RAM 8 GB, dan SSD 477 GB. Waktu eksekusi diukur menggunakan pustaka std::chrono, sedangkan penggunaan memori diukur dengan fungsi getMemoryUsage(). Hasil menunjukkan bahwa Introsort secara konsisten menjadi algoritma paling cepat untuk seluruh ukuran data. Pada 10.000 elemen, rata-rata waktu eksekusi Introsort sekitar 2,88 ms, sedangkan Bubble sort dan Selection sort masing-masing sekitar 1.303 ms dan 1.067 ms. Dengan demikian, Introsort sekitar 450 kali lebih cepat daripada Bubble sort dan 370 kali lebih cepat daripada Selection sort pada skala data besar, sementara perbedaan penggunaan memori di antara ketiga algoritma berada di bawah 0,15 MB. Temuan ini mengindikasikan bahwa pemilihan algoritma modern seperti Introsort sangat disarankan untuk pengurutan data berukuran besar di C++, sedangkan algoritma tradisional lebih tepat digunakan untuk skenario pembelajaran atau dataset kecil
ANALISIS EFISIENSI MEMORI DAN WAKTU EKSEKUSI ALGORITMA QR SORT DAN BUBBLE SORT DALAM C++ Adi Handika; Hafizhah Azzahrani; Rizqi Karima; Mualim Mualim; Imam Prayogo Pujiono
JEIS: Jurnal Elektro dan Informatika Swadharma Vol 6, No 1 (2026): JEIS EDISI JANUARI 2026
Publisher : Institut Teknologi dan Bisnis Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jeis.vol6no1.1060

Abstract

Sorting is a fundamental process in computer science that significantly impacts data management and computational efficiency. This study aims to compare the performance of two sorting algorithms, QR Sort and Bubble sort, implemented in C++. The comparison analyzes and evaluates the two algorithms in terms of execution time and memory usage efficiency across datasets of varying sizes. This study uses a quantitative experimental method, with three dataset sizes: 100, 1,000, and 10,000 integer elements, which are run repeatedly under identical conditions to ensure consistent results. The experiments measure the average execution time (in microseconds) and estimated memory usage (in kilobytes) for each algorithm. The results show that QR Sort, which applies a non-comparative quotient–remainder approach, performs significantly faster than Bubble sort, which is comparative, especially as the data size increases. On large datasets, QR Sort outperforms Bubble sort by more than 100 times in execution time. This speed increase is accompanied by higher memory consumption, as QR Sort requires additional structures such as a bucket and counting arrays, whereas Bubble sort uses only minimal memory. Overall, these findings confirm that QR Sort is better suited for large-scale data processing where speed is a priority. At the same time, Bubble sort remains more efficient for small datasets or educational purposes due to its simplicity and low memory requirements. This study emphasizes the need to choose an appropriate sorting algorithm based on dataset characteristics and available system resources.Pengurutan (Sorting) adalah proses dasar dalam ilmu komputer yang memiliki pengaruh besar terhadap pengelolaan data dan efisiensi komputasi. Penelitian ini bertujuan untuk membandingkan kinerja dua algoritma pengurutan, yaitu QR Sort dan Bubble sort, yang diimplementasikan menggunakan bahasa pemrograman c++. Perbandingan dilakukan untuk menganalisis dan mengevaluasi kedua algoritma tersebut dari segi efisiensi waktu eksekusi dan penggunaan memori ketika diterapkan pada kumpulan data dengan ukuran yang berbeda-beda. Penelitian ini menggunakan metode eksperimen kuantitatif, dengan tiga skala dataset: 100, 1.000, dan 10.000 elemen bilangan bulat, yang dijalankan berulang kali dalam kondisi identik untuk memastikan konsistensi hasil. Percobaan mengukur rata-rata waktu eksekusi (dalam mikrodetik) dan perkiraan penggunaan memori (dalam kilobyte) untuk masing-masing algoritma. Hasil penelitian menunjukkan bahwa QR Sort, yang menerapkan pendekatan non-comparative quotient–remainder, bekerja jauh lebih cepat dibandingkan Bubble sort yang bersifat comparative, terutama saat ukuran data meningkat. pada dataset besar, QR Sort mengungguli Bubble sort dengan kecepatan lebih dari 100 kali lipat dalam waktu eksekusi. Peningkatan kecepatan ini disertai dengan konsumsi memori yang lebih tinggi karena QR Sort memerlukan struktur tambahan seperti bucket array dan counting array, sedangkan Bubble sort hanya menggunakan memori dalam jumlah minimal. Secara keseluruhan, temuan ini menegaskan bahwa QR Sort lebih cocok untuk pemrosesan data berskala besar di mana kecepatan menjadi prioritas, sedangkan Bubble sort tetap lebih efisien untuk dataset kecil atau keperluan edukatif karena kesederhanaannya dan kebutuhan memori yang rendah. Penelitian ini menekankan perlunya memilih algoritma pengurutan yang sesuai berdasarkan karakteristik dataset dan sumber daya sistem yang tersedia
EFISIENSI MEMORI DAN WAKTU: ARRAY SORTING ALGORITHM VS ALGORTIMA PENGURUTAN TRADISIONAL MENGGUNAKAN PYTHON Muhammad Zaki Musyaffa; Karunia Raharjo; Muhammad Faiz; Satriaji Ammarulloh; Imam Prayogo Pujiono
JEIS: Jurnal Elektro dan Informatika Swadharma Vol 5, No 2 (2025): JEIS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jeis.vol5no2.785

Abstract

The development of information technology has transformed data storage from physical to digital formats. However, digital data is difficult to access and verify without an effective sorting mechanism in place. This study compares the memory usage efficiency and execution time of the array sorting algorithm with two traditional sorting algorithms, bubble sort and quick sort, using a quantitative comparative method, all implemented in Python. Experiments were conducted on random numerical data sets of 100, 1.000, and 10.000 elements. Results show that the Array Sorting Algorithm excels in computational speed, with average times of 231.500, 352.500, and 1.214.150 nanoseconds for each data scale. However, it requires slightly larger memory (2.320 – 84.304 bytes). In contrast, Bubble Sort is the slowest but most memory-efficient, while Quick Sort is intermediate in both aspects. On the other hand, the Array Sorting Algorithm recorded relatively higher memory usage compared to the traditional sorting algorithms, Bubble Sort and Quick Sort. Based on these findings, algorithm selection should be based on the primary need. The Array Sorting Algorithm can be used when execution speed is a priority, and Bubble Sort is suitable for environments with memory constraints and small datasets. At the same time, Quick Sort offers a balance between speed and memory usage efficiency.Perkembangan teknologi informasi telah mentransformasi penyimpanan data dari format fisik ke digital. Namun, tanpa mekanisme pengurutan yang efektif, data digital sulit diakses dan diverifikasi. Penelitian ini membandingkan efisiensi penggunaan memori dan waktu eksekusi Array Sorting Algorithm dengan dua  algoritma pengurutan tradisional, Bubble Sort dan Quick Sort menggunakan metode komparatif kuantitatif., semuanya diimplementasikan dalam bahasa Phyton. Eksperimen dilakukan pada kumpulan data numerik acak berukuran 100, 1.000, dan 10.000 elemen. Hasil menunjukkan bahwa Array Sorting Algorithm unggul dalam kecepatan komputasi dengan rata-rata waktu 231.500, 352.500, dan 1.214.150 nanodetik untuk masing-masing skala data, namun memerlukan memori sedikit lebih besar (2.320 – 84.304 byte), sedangkan Bubble Sort paling lambat namun paling hemat memori, dan Quick Sort menengah di kedua aspek. Di sisi lain, Array Sorting Algorithm mencatatkan pemakaian memori yang relatif lebih tinggi dibanding algoritma pengurutan tradisional Bubble Sort dan Quick Sort. Berdasarkan hasil dari temuan ini pemilihan algoritma harus didasarkan pada kebutuhan utama, Array Sorting Algorithm bisa digunakan saat kecepatan eksekusi menjadi prioritas, Bubble Sort cocok untuk lingkungan dengan keterbatasan memori dan dataset berukuran kecil, sedangkan Quick Sort menawarkan keseimbangan antara kecepatan dan efisiensi penggunaan memori
ANALISA EFISIENSI WAKTU KOMPUTASI DAN PENGGUNAAN MEMORI PADA ENAM ALGORITMA PENGURUTAN Nafisha Putri Arsita; Rahma Syarifa; Tsaqib Fahmi Ahmad; Imam Prayogo Pujiono
JEIS: Jurnal Elektro dan Informatika Swadharma Vol 6, No 2 (2026): JEIS EDISI JULI 2026
Publisher : Institut Teknologi dan Bisnis Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jeis.vol6no2.1071

Abstract

This study aims to analyze and compare the performance of six sorting algorithms (Tim Sort, Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, and Merge Sort) in terms of computational time efficiency and memory usage. Implementation was carried out in Java using Visual Studio Code as the development environment, and testing was conducted on a device with an Intel Core i7-8665U (1.02 GHz) processor, 8 GB of RAM, a 64-bit Operating System, and a 256 GB SSD. All algorithms were tested using three different dataset sizes, namely 100, 1,000, and 5,000 data points, each with three repetitions to obtain more accurate results. Based on the test, Tim Sort showed the most efficient performance, maintaining stable computational time and memory usage across all dataset sizes. Quick Sort and Merge Sort have also been shown to perform well, especially for large data, in contrast to Bubble Sort, Selection Sort, and Insertion Sort, which become less efficient as data grows due to significantly increased processing time. Overall, this study concludes that Tim Sort is the most optimal algorithm in terms of speed and memory efficiency. Quick Sort and Merge Sort are recommended for sorting large-scale data. At the same time, basic algorithms like Bubble Sort, Selection Sort, and Insertion Sort are better suited to small datasets or to learning sorting concepts.Penelitian ini bertujuan untuk manganalisis dan membandingkan performa enam algortima sorting (Tim Sort, Bubble Sort, Selection Sort, Insertion Sort, Quick Sort, dan Merge Sort) dari aspek efisiensi waktu komputasi dan penggunaan memori. Implementasi dilakukan menggunakan Bahasa pemrograman Java melalui Visual Studio Code sebagai lingkungan pengembangan, dan pengujian dijalankan pada perangkat dengan spesifikasi Intel Core i7-8665U (1,02 GHz), RAM 8 GB, Sistem Operasi 64-bit, serta SSD 256 GB. Semua algoritma diuji menggunakan tiga perbedaan ukuran dataset, yaitu 100 data, 1.000 data, dan 5.000 data, masing-masing dengan tiga kali pengulangan untuk mendapatkan hasil yang lebih akurat. Berdasarkan pengujian ditemukan bahwa Tim Sort menunjukkan kinerja yang paling efisien, mempertahankan waktu komputasi, dan pemakaian memori yang stabil pada semua ukuran dataset. Quick Sort dan Merge Sort juga terbukti memiliki performa kuat, khususnya ketika menangani data berukuran besar, berbeda dengan Bubble Sort, Selection Sort, dan Insertion Sort yang menjadi kurang efisien karena peningkatan waktu proses yang signifikan seiring pertumbuhan data. Secara keseluruhan, penelitian ini menyimpulkan bahwa Tim Sort merupakan algoritma yang paling optimal dalam aspek kecepatan dan efisiensi memori. Adapun Quick Sort dan Merge Sort direkomendasikan untuk pengurutan data dalam skala besar, sedangkan algoritma dasar seperti Bubble Sort, Selection Sort, dan Insertion Sort lebih sesuai digunakan untuk dataset kecil atau keperluan pembelajaran konsep pengurutan
Co-Authors A'izatul Haira, Nala Abdi Cahya Subekti Abdul Majid Abu Hanif I. F. Adhiel Rahma Adhiel Adi Handika Adi Handika Adib' Aunillah Fasya Agung Bakti Agung Prihandono Agung Prihandono Agustina, Risma Wati Agyztia Premana Ahmad Abdussalaam Ahmad Aufal Marom Ahmad Burhanuddin, Ahmad Ahmad Ta'rifin Ahmad Turmudi Ahmad Turmudi Aisyah Aisyah Aisyah Nur Ramadhani Ajib Susanto Akhmad Aufa Syukron Alawiyah, Vina Vitri Alfisyah, Ahmad Alfiyan Firmansyah Alina Putri As Salwa Alisha Zulfa Salsabila Amelya Agustin Andika Faza Setiawan Andy Rachman Anugrah, Anggita Dwi Anwar, M. Shokhib Anzaini, Sayla Salma Apriani, Nadisa Ardansyah , Dawam Ardhiartha P.U., Surya Arditya Prayogi Ariza, Said Fachri Armaylis Arfa Arum, Dewi Sekar Asfahani, Asfahani Atha Harshavardhana Aulia Miza, Nur Ayon Diniyanto Azazi, Firly Maulana Bagus Prasetyo Santoso Bangkit Rizka Hidayatullah Berliannanda, Anugrah F. Bintan Kamila Brilianu Sentanu Vito Chomsah Rachmawati Christy Atika Sari Dara Amalia Azzahrah Darutama, Ardiyan Dea Karuniawati Delviera Gea Florida Desi Lestari, Desi Desta Adyangga Saputra Diah Puspitaningrum Dian Sukmawati Sudrajat Dicky Anggriawan Nugroho Dicky Anggriawan Nugroho Dicky Anggriawan Nugroho Dicky Anggriawan Nugroho Dicky Anggriawan Nugroho Didi Purnomo Edi Suharyadi Edita Rayhanah Fairuz Eka Puji Rahayu Eko Hari Rachmawanto Eliza, Naila Nur Elvina Rizqi Emi Nurlaela Erwin Erwin Fachri Ali Fachri Ariza, Said Fadilah Zahra Dwi Kinanti Fadilah, Gita Faiz, Muhammad Fakhrul Arhabur Rizqi Fatkhatun Naela Ferida Rahmawati Fiantika Mutia Rahma Fida Maisa Hana Fida Maisa Hana Fira Sulistia Firda Aulia Izzati Firda Rona Syahira Firdaus Perdana Firdausi, Muh Izzat Firdausi, Muhammad Izzat Fitroh, Qorry Aina Fuad Naufal Ihsan Furqon Lanang Galang Satya Aulia Habibah, Adinda Destiani Hafizhah Azzahrani Haiqa Virly Azzahra Hakim Elkominoki, M. Lukman Hamdi Yahya, Muhammad Harun Ibrahim Abdallah Mohammad Hikmah Sofyan, Nurul Himawan, Inggil Ibnu Utomo Ihsanul Ahadin, Akbar Ikhsan Maulanaa Ikhsanuddin, Rohmatulloh Muhamad Ilham Dwijokangko Imam Prawira Imas Saffanatul Aminah Imas Saffanatul Aminah Imtinan Hanazain Indra Kurniawan Irfan Ali, Muhammad Jainul Arifin Jainul Arifin kamilah, Arina Kharisatul Karunia Raharjo Karunia Raharjo Kholifatun, Isnaeni Kuat Ismanto Kuat Ismanto Lilik Riandita Lilik Riandita Lilina Dwi Anastasya Lina Azaria Linda Oktaviani Lukman Shodik M Aldy Zulfa Saputra M. Abyan Nuha M. Adin Setyawan M. Adin Setyawan M. Adin Setyawan M. Adin Setyawan M. Akmal Fatkhan Rifqi M. Arsyad Alkadafi M. Azka Failandri M. Lukman Hakim Elkominoki M. Syaifuddin M. Wildan Ahfadh Maesyaroh Maftuhin Meila Fitri Amin Moch. Nazril Ilham Mochamad Iskarim Moh. Sugeng Solehuddin Moh. Syaifuddin Moh. Syaifuddin Moh. Syaifuddin Mohammad Syaifuddin Mualim Mualim Muchammad Soufwan Fauzi Mufidah, Haniatul Muhamad Ridwan Muhammad Agus Muhammad Anifan Muhammad Azka Bani Shalih Muhammad Fabian Ardhiansyah Muhammad Faiz Muhammad Farras Abiy Muhammad Noval Syafiq Sofi Muhammad Rikzam Kamal Muhammad Rikzam Kamal Muhammad Risqi Agung Pratama Muhammad Rofi Nur Fasyih Muhammad Yusuf Rusty Al Badar Muhammad Zaki Musyaffa Muhammad Zidni Nur Muqtafiy Muhammad Mutiara Sofia Ramadhani Nabil Yudha Syahputra Nadia Faradhillah, Nadia Nafisa Purnamasari Nafisha Putri Arsita Naila Mifrotul Ula Nazwa Gista Aulia Nazwa Nevi Dwi Apriyanti Nisa , Nabila Luthfiyatun Noorma Fitriana M. Zain Nugroho, Dicky Anggriawan Nukman Zadi Nur Azizah Nur Fatikhatul Jannah Nur Khotimah Nurifah Mumtazah Nuril Lutvi Azizah Nurul Hikmah Sofyan Nurul Hikmah Sofyan Nurul Husnah Mustika Sari Pasha Ferbitama Perdana, Firdaus Prabowo, Dimas Setiaji Pratomo Cahyo Kurniawan Putri, Adhystha Az-Zahra Putri, Pramesti Fadhila Pamuji Qurrota A’yun Raesya Satria Mikaelana rahayu, eka puji Rahma Syarifa Rahmawan Bagus Trianto Rakhiel Ghina Suraya Rangga Dzikri Fardiansyah Rangga Dzikri Fardiarsyah Regina Nur Aeni Rhischa Assabet Shilla Rhischa Assabet Shilla, Rhischa Assabet Riandita, Lilik Richa Qonitatin Lazaba Riki Nasrullah Riki Nasrullah Riki Nasrullah Riki Nasrullah Riyadi, Ridho Rizal, Muhamad Rizaludin Rizki Ramadani Rizqi Karima Rofiah, Nurul Hidayati Rohmad Abidin Rohmad Abidin Rohmah, Syifa Rohmatulloh Muhamad Ikhsanuddin Roshikhotun Muthi'ah Sabrina Ahmad Sabrina, Arum Rahma Putri Saffanatul Aminah, Imas Safinatunaja , Dhifa Saizi, Muhammad Salman Salma Aulia Salma Salsabila Amanda Salwa Salwa Satriaji Ammarulloh Shabina Nur Fatmaluna Shofiani, Rissa Singgih Setiawan Singgih Setiawan Singgih Setiawan Singgih Setiawan Sofia, Lailatul Sofiana, Sofa Sopiah, Sopiah Suci Lestari suharyadi suharyadi Syahda, Talitha Ulima Syaifuddin, Moh Syifa Rohmah Sylva Fahri Nailannaja Syukron, Akhmad Aufa Talitha Falakh Tasya Tiara Ananda Maulidia Tsalisa Yuliyanti Tsalisa Yuliyanti Tsaqib Fahmi Ahmad Ummy Fatimah Utomo, Surya Ardhiartha Putro Vierri Daffa Alief Widodo Hami Widodo Hami Wijanarto Wijanarto Winarsih, Nurul Anisa Sri Yasmin, Nazhifa Naura Yoga Religia Yunita Lisnaningtyas Utami Zahra Khoirunnisa Zahwa Arizza Arthamevia Zaki Kafila Pamungkas Zuhrufillah, Khafifah Zulmi Aulia Azhari Abbas