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All Journal Jurnal Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Inotera Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Eduscience (JES) JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Jurnal Mandala Pengabdian Masyarakat Jurnal AbdiMas Nusa Mandiri Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Minfo Polgan (JMP) Journal of Renewable Energy, Electrical, and Computer Engineering sudo Jurnal Teknik Informatika TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Informasi Dikdimas : Jurnal Pengabdian Kepada Masyarakat Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Jurnal Ilmu Komputer dan Sistem Informasi Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS)
Said Fadlan Anshari
Universitas Malikussaleh
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Comparison of Support Vector Machine and Naïve Bayes Algorithms in Sentiment Analysis of Tiktokshop Application User Reviews Agus Maula Rizki; Bustami Bustami; Said Fadlan Anshari
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 5 No. 1 (2025): March 2025
Publisher : Institute for Research and Community Service (LPPM), Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v5i1.21342

Abstract

This study presents a comparative analysis of Support Vector Machine (SVM) and Naïve Bayes algorithms for sentiment analysis of TikTokShop application user reviews. As TikTokShop emerges as an innovative platform integrating social media with e-commerce, understanding user sentiments becomes crucial for both consumers and businesses. A balanced dataset of 3,000 user reviews (1,000 positive, 1,000 neutral, and 1,000 negative) was collected through web scraping from Google Play Store. Following comprehensive preprocessing including cleansing, case folding, normalization, tokenization, stopword removal, and stemming, the data was vectorized using TF-IDF. Performance evaluation utilized accuracy, precision, recall, F1-score, confusion matrix, and 10-fold cross-validation. Results demonstrate that SVM consistently outperformed Naïve Bayes with higher accuracy (68.86% vs. 64.48%), precision (68.43% vs. 64.19%), and F1-score (68.58% vs. 62.46%). SVM exhibited balanced classification across all sentiment categories, while Naïve Bayes excelled at identifying negative sentiments (94.1% accuracy) but struggled significantly with neutral reviews (38.5%). Despite SVM's superior performance, Naïve Bayes demonstrated remarkable computational efficiency, with training time 224 times faster than SVM. The study reveals complementary strengths between the algorithms, suggesting potential value in ensemble approaches. These findings contribute to the understanding of sentiment analysis in video-based e-commerce platforms and provide valuable insights for businesses seeking to leverage user feedback for improved decision-making.
IMPLEMENTASI FRAMEWORK CODEIGNITER DALAM PENGEMBANGAN SISTEM MANAJEMEN DATA DAN INFORMASI ALUMNI BERBASIS WEB Rizki Suwanda; Said Fadlan Anshari; Rizal Rizal
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 6 No. 2 (2022): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2022
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v6i2.10470

Abstract

Data alumni salah unsur yang paling penting dalam peningkatan mutu perguruan tinggi. Alumni menjadi perpanjangan tangan sebagai pembentuk jaringan kerja yang diharapkan dapat membuka relasi instansi untuk memudahkan para mahasiswa dan alumni  saling berbagi informasi dalam dunia kerja dan ilmu pengetahuan. Sebagai upaya pengembangan dan peningkatan sistem manajemen informasi alumni diperlukan sebuah sistem pengelolaan data yang bisa diakses dengan waktu yang tidak terbatas sesuai dengan kebutuhan. Pengembangan sistem berbasis web dengan menerapkan Codeigniter berbasis php dengan menerapkan konsep  MVC. Codeigniter menjadi sebuah toolkit yang diminati ditujukan kepada pengembang aplikasi web dalam bahasa PHP dengan berbagai macam library yang disediakan yang dapat mempermudah dalam pengembangan aplikasi web. Hasil penelitian ini dapat diterapkan pada penelusuran data alumni dengan sistem manajemen data alumni berbasis web yang terkomputerisasi dan terstruktur dapat memudahkan program studi maupun universitas dalam mengelola data dan informasi para lulusan.
Classification of Hospital Stay Duration for Schizophrenia Patients at RSUD Muyang Kute Using a Combination of C4.5 and Particle Swarm Optimization Putri Agustina Dewi; Munirul Ula; Said Fadlan Anshari
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25930

Abstract

Schizophrenia is a chronic mental disorder that often requires inpatient care, so an increase in the number of patients can lead to limited bed capacity in psychiatric wards. This study aims to classify the length of hospital stay for schizophrenia patients to support room requirement planning at RSUD Muyang Kute using the C4.5 algorithm optimized with Particle Swarm Optimization (PSO). The dataset consists of 657 medical records of inpatient schizophrenia cases from February 2023 to March 2025, categorized into three length-of-stay classes: short (1–5 days), medium (6–10 days), and long (>10 days). The C4.5 algorithm is used to construct a decision tree model based on historical data, while PSO is employed as an optimization method to improve the model configuration. The evaluation uses classification accuracy and Mean Absolute Percentage Error (MAPE) for room demand estimation. The results show that both the C4.5 and C4.5–PSO models achieve similarly high accuracy on the test data, while the manual MAPE calculation for room demand estimation yields a value of 52.66%. In contrast, the MAPE calculated by the system is 0.00% in the test scenario because all classes in the test data are correctly predicted. The web-based decision support system developed using Python and Streamlit is able to automatically provide predictions of length of stay and estimates of the required number of psychiatric beds at RSUD Muyang Kute.
Analysis of X and Threads Responses Based on Single Keywords Using Graph Neural Network Sinaga, Rifky Fahriza; Rizal, Rizal; Anshari, Said Fadlan
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 21, No 1 (2026): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v21i1.29143

Abstract

This study analyzes user responses on the social media platforms X and Threads regarding radicalism, based on the single keyword "radicalism." Differing interaction characteristics between the two platforms motivate a comparison of network structure using a graph-based approach and Graph Neural Networks (GNN). Data were collected through scraping of public content on X and Threads, with 5,000 raw posts each, yielding 1,327 reply interactions on X and 1,273 on Threads. Research stages included data collection, preprocessing, construction of the user-post graph, network metric analysis, and implementation of a Graph Autoencoder with a Graph Convolutional Network (GCN) encoder to generate node embeddings. The resulting graphs comprised 1,471 nodes and 1,223 unique edges for X, and 1,491 nodes and 1,081 unique edges for Threads, with X showing a denser structure (density 0.000565; average degree 1.663) than Threads (density 0.000487; average degree 1.450), while Threads was more fragmented (412 weak components versus 299 on X). The Graph Autoencoder was evaluated via link prediction using AUC and Average Precision (AP): X achieved AUC 0.5397 and AP 0.5879, slightly above the random-guessing baseline, while Threads achieved AUC 0.4987 and AP 0.5507, indicating a structure harder to reconstruct due to fragmentation. These quantitative results reinforce the network-metric findings that X forms a more connected network while Threads fragments into smaller groups. Practically, the findings offer an empirical basis for a decision-support system monitoring radicalism-related discourse, favoring dominant-cluster monitoring on X and parallel, cross-cluster monitoring on Threads. This study does not aim to detect or label accounts or content as radical, but to analyze interaction patterns and network characteristics of user responses.
Student Creativity Education in Plastic Waste Processing Innovation Tulus Setiawan; Rizki Suwanda; Said Fadlan Anshari; Nur Fazri Husna; Tiara Sartika
DIKDIMAS : Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 3 (2024): DIKDIMAS : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Asosiasi Profesi Multimedia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/dikdimas.v3i3.350

Abstract

This community service activity aims to address environmental problems, emphasizing project-based learning, where participants are invited to solve real-world problems in their environment. The implementation of the Kurikulum Merdeka (Independent Curriculum) offers a great opportunity to utilize the Project for Strengthening Pancasila Student Profiles (P5) as a platform to encourage student involvement in environmental issues. One tangible form of P5 implementation is through a project converting plastic waste into oil. The activity was conducted at SMA Negeri 5 Kota Lhokseumawe, employing the creative education method for plastic waste processing, including education and training for teachers and students, as well as project assignments for processing plastic waste through the pyrolysis process. The results of the community service activity showed that more than 80% of respondents rated the educational activities, such as material delivery, awareness-raising, and providing new insights on plastic waste processing, as excellent. Moreover, 95% expressed satisfaction with the implementation of the plastic waste processing project via the pyrolysis process, which successfully provided educational, technical, and motivational experiences to participants. Education and the plastic waste processing project have proven to offer practical solutions that not only enhance students' knowledge and skills but also drive behavioral and attitudinal changes toward the environment.
Application of Multiple Linear Regression Method for Predicting Fish Production Based on Cultivation Type Hendra Putranta Limbong; Dahlan Abdullah; Said Fadlan Anshari
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.717

Abstract

One of the contributors to Indonesia's economy is the fisheries sector, which has a high potential for development. Fisheries are a highly promising subsector for development in Indonesia's growth efforts. Based on data from the Central Bureau of Statistics of Dairi Regency, three types of aquacultures remain actively utilized in each subdistrict: ponds/freshwater ponds and paddy fields. This research aims to develop a fish production prediction system based on aquaculture types using the Multiple Linear Regression method. The accuracy of the prediction results will be measured using the Mean Absolute Percentage Error (MAPE). The results of this study indicate that in almost every subdistrict, especially pond aquaculture, the MAPE value is 20%, which means it has good accuracy. However, exceptions are found in the Siempat Nempu Hulu subdistrict, which has a MAPE value of 34.29%, and the Silahisabungan subdistrict, which has a MAPE value of 43.78%. Despite these values, they are still categorized as sufficient since they are 50%. The lower the MAPE value, the more accurate the prediction results. The findings of this research show that the multiple linear regression method can be considered correct. For future predictions, some results show negative values. For instance, in Silimapunggapungga subdistrict, a decline in production is predicted for 2024 with -114.779 tons and 2025 with -134.316 tons. The pessimistic prediction results are caused by the decrease in the X2 variable (area size), leading to a minor Y (production) value, potentially becoming negative if the contribution of X2 is no longer sufficient to balance the values of X1 (b1) and a. On the other hand, the Lae Parira subdistrict is predicted to experience an increase in production in 2024 by 87.024 tons and in 2025 by 84.380 tons. This system is implemented using the Python programming language. It is expected to help relevant stakeholders understand production trends and enhance the efficiency of fisheries resource management in the Dairi Regency.
Klasifikasi Kesiapan Petani dalam Penerapan Teknologi Digital Pencatatan Hasil Panen Menggunakan Algoritma K-Nearest Neighbor Zakia Ulfa; Taufiq Taufiq; Said Fadlan Anshari
sudo Jurnal Teknik Informatika Vol. 5 No. 3 (2026): Edisi September
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/sudo.v5i3.2055

Abstract

Kesiapan petani menerapkan teknologi digital pencatatan hasil panen menjadi faktor penting bagi transformasi pertanian berbasis data di Kecamatan Makmur, Kabupaten Bireuen. Penelitian ini mengklasifikasikan tingkat kesiapan petani menggunakan algoritma K-Nearest Neighbor (K-NN) serta mengevaluasi akurasi hasil klasifikasinya. Pendekatan yang digunakan adalah kuantitatif dengan metode eksperimen. Data diperoleh melalui kuesioner terhadap 500 petani dengan sembilan variabel, yaitu usia, jenis kelamin, tingkat pendidikan, pengalaman bertani, kepemilikan perangkat digital, luas lahan, pendapatan petani, keikutsertaan penyuluhan, dan literasi digital. Data tersebut dibagi menjadi 400 data latih dan 100 data uji. Pada tahap klasifikasi, data latih diseimbangkan menggunakan Synthetic Minority Over-sampling Technique (SMOTE), kemudian dilakukan pengujian 5-fold cross validation untuk menentukan nilai K terbaik, yaitu K=3. Dari 500 data petani, sebanyak 88,80% termasuk kategori "Siap", 9,60% kategori "Cukup Siap", dan 1,60% kategori "Belum Siap". Pengujian confusion matrix terhadap 100 data uji menghasilkan akurasi sebesar 69%, dengan performa terbaik pada kelas "Siap" berupa F1-Score 0,827, sedangkan kelas "Cukup Siap" hanya 0,063 akibat data yang terbatas. Selain itu, penelitian ini menghasilkan sistem informasi berbasis web bernama "PanenKu" yang menyediakan fitur pencatatan data tanaman, lahan, hasil panen, riwayat, laporan, grafik, dan analisis, serta diuji menggunakan black box testing pada 20 skenario dengan seluruh fitur berjalan sesuai harapan.
Co-Authors Agus Maula Rizki Al-Ghiyats, Said Ananda, Ria Angkasa, M. Ishlah Buana Arnawan Hasibuan Aryo Wibisono Putra Asrillah Asrillah Bustami Bustami Chan, Fajar Ricaldi Dahlan Abdullah Dani Ramdani Dinda, Dinda Fadlisyah Fadlisyah Fadlisyah Fahrezi, Irgi Fanita, Fanita Hendra Putranta Limbong Hidayatsyah Hidayatsyah Islami Fatwa Juliar Husriansyah Juliawati Khairi, Abil Kurniawati Kurniawati Lidya Rosnita M Ishlah Buana Angkasa M. Rafli Al Thoriq Mustafa Maryana Maryana Maulana, O.K. Muhammad Majid Melita Saldila Mhd Firza Ryzaaldy Muchlis Abdul Muthalib Muh Fahrudin Alawi Muhammad Arrayyan Muhammad Daud Muhammad Fikry Muhammad Hafizal Mukti Qamal Mukti Qamal Munirul Ula Nadia Rizatul Riski Nur Fazri Husna Pohan, Fadlyani Putri Agustina Dewi Putri, Nazirah Allisya Rahmatin Nisak Rini Meiyanti Rizal Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal, Reyhan Achmad Rizki Suwanda Rizki, Sukma Rizky Putra Fhonna Rozzi Kesuma Dinata Safwandi Safwandi Sahputra, Irfan Samudera, Brucel Duta Sandy, Cut Lika Mestika Sinaga, Rifky Fahriza Sudirman Sudirman Sujacka Retno Sukiman, T. Sukma Achriadi Sukma Rizki Syahriani Putri Ayu Syibral Malasyi, Syibral Taufiq Taufiq Tiara Sartika Tri Ramdhany Tulus Setiawan Wardina Ningsih Yasin, Fijri Ahmad Zahratul Fitri Zainul Mujtahid Zakia Ulfa Zalfie Ardian Zara Yunizar Zarayunizar Zarayunizar