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All Journal AKADEMIKA Jurnal Nasional Komputasi dan Teknologi Informasi PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Specta Journal of Technology KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Teknologi Informasi Cyberku Jurnal Ilmu Komputer Jurnal Sistem Informasi dan Bisnis Cerdas Jurnal Abdimas Terapan Jurnal Teknologi Informasi Indonesia Jurnal Penelitian Pendidikan Indonesia Riau Jurnal Teknik Informatika Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Repositor
Alter Lasarudin
Universitas Muhammadiyah Gorontalo
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Neuroscience Public Health Social Sciences Transportation Other

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Evaluasi Sistem Informasi Menggunakan Model HOT-FIT Pada Sistem Penerimaan Mahasiswa Baru di Lingkungan Universitas Muhammadiyah Gorontalo Syahrial, Syahrial; Larote, Yonis; Lasarudin, Alter; Olii, Muhammad Ramdhan
Jurnal Ilmu Komputer (JUIK) Vol 4, No 2 (2024): JUNE 2024
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v4i2.3315

Abstract

This research was conducted to evaluate the website for new student admissions at the Universitas Muhammadiyah Gorontalo. Evaluation was carried out by applying the HOT-Fit model method to 4 components. Focus evaluation on technological, human, organizational and benefits components. The aim is to determine the level of usability of the Universitas Muhammadiyah Gorontalo New Student Admissions System. The research method was carried out quantitatively by collecting questionnaire data from the committee and registrants. The HOT-Fit model is applied in determining system evaluation indicators. The results of the HOT-Fit model approach on 8 variables are good. There is a test value for System Quality (KS) with a value of 73.8 (Good), then Service Quality (KL) with a value of 82.0 (Very Good), then Information Quality (KI) with a value of 72.3 (Good), then System Usage (PS) with a value of 77.9 (Good), then User Satisfaction (KP) with a value of 74.2 (Good), then Organizational Environment (LO) with a value of 82.7 (Very Good), then Organizational Structure (SO ) with a score of 66.7 (Good), and System Benefits (MS) with a score of 82.2 (Very Good). Categories in 8 variables are good to use based on the HOT-Fit method with a value obtained of 76.5. The Usability Testing results are good for use based on the HOT-Fit method and the assessment of new students as respondents.
Digital Library Universitas Muhammadiyah Gorontalo Antupetu, Rastin; Abas, Mohamad Ilyas; Lasarudin, Alter; Lamusu, Rizal
Jurnal Ilmu Komputer (JUIK) Vol 4, No 1 (2024): FEBRUARY 2024
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v4i1.2797

Abstract

This research was conducted at the Library of Universitas Muhammadiyah Gorontalo. The aim is to design a web-based library information system with a digital concept (Digital Library). The design of the Digital Library information system of Universitas Muhammadiyah Gorontalo, with a system design model using the prototyping model, provides a forum for students and lecturers, especially those on the campus of Universitas Muhammadiyah Gorontalo to publish research results in the form of theses, journals, and theses into this digital library system. With this digital library it will facilitate access for prospective graduates, both the academic community and academics outside the campus, who will take literature as a reference for the final report or thesis. Provide convenience in disseminating useful information or knowledge and help students conduct research.
PENERAPAN DATA MINING DALA MENGKLASIFIKASI RESIKO PENYAKIT STROKE MENGGUNAKAN METODE MACHINE LEARNING Bakari, Sitti Nurfatimah; Lasarudin, Alter; Hasyim, Wahyudin
Jurnal Ilmu Komputer (JUIK) Vol 5, No 2 (2025): JUNE 2025
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v5i2.4186

Abstract

Stroke adalah kondisi saraf yang ditandai dengan tanda klinis yang berkembang pesat, termasuk gangguan saraf lokal dan global yang parah, bertahan selama 24 jam atau lebih, dan berakibat fatal ketika tidak ada penyebab yang jelas selain vaskular. Penelitian ini bertujuan untuk menerapkan algoritma Support Vector Machine (SVM) dan Artiticial Neural Network (ANN) untuk mengklasifikasi risiko penyakit stroke dan mengukur tingkat akurasi dari masing-masing algoritma. Hasil penelitian menggunakan algoritma Support Vector Machine dan Artiticial Neural Network dengan jumlah record data 200 maka akan diperoleh nilai akurasi Support Vector Machine sebesar 97.46%, dan tingkat akurasi Artiticial Neural Network sebesar 98.75%. Paling algoritma Artificial Neural Network mendapatkan nilai akurasi yang tertinggi dibandingkan algoritma Support Vector Machine dalam pengklasifikasian data risiko penyakit stroke.
PENERAPAN DATA MINING UNTUK PREDIKSI PENJUALAN PRODUK TERLARIS MENGGUNAKAN METODE ANN DAN LSTM Hasanah, Ika Nur; Lasarudin, Alter; Hasyim, Wahyudin
Jurnal Ilmu Komputer (JUIK) Vol 5, No 2 (2025): JUNE 2025
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v5i2.4180

Abstract

Toko Setia Keramik yang menjual berbagai jenis keramik sedang menghadapi tantangan dalam menentukan produk keramik terlaris akibat data penjualan yang belum diolah secara optimal. Penelitian ini bertujuan untuk menerapkan algoritma Artificial Neural Network (ANN) dan Long Short-Term Memory (LSTM) untuk memprediksi penjualan produk keramik terlaris serta mengukur tingkat akurasi prediksi masing-masing algoritma. Data yang digunakan mencakup penjualan lima jenis keramik selama tiga tahun (2021–2023). Hasil penelitian menunjukkan bahwa algoritma ANN memberikan prediksi terbaik untuk pola jangka pendek dengan tingkat kesalahan Mean Square Error (MSE) yang rendah, sementara algoritma LSTM unggul dalam memprediksi pola jangka panjang. Penerapan kedua algoritma ini mampu membantu Toko Setia Keramik dalam menentukan jenis keramik terlaris sehingga pengelola toko dapat mengelola stok barang secara lebih efisien dan meminimalisir kekurangan maupun kelebihan stok, serta mengantisipasi tren penjualan di masa depan. Dengan demikian, penelitian ini memberikan kontribusi penting dalam optimalisasi manajemen penjualan menggunakan metode prediksi berbasis data mining.
O OPTIMALISASI NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK MEMPREDIKSI LAMA PENYINARAN MATAHARI DALAM MEMENUHI KEBUTUHAN ENERGI Hasyim, Wahyudin; Lasarudin, Alter
Jurnal Teknologi Informasi Indonesia (JTII) Vol 4 No 2 (2019): Jurnal Teknologi Informasi Indonesia (November)
Publisher : JURNAL TEKNIK INFORMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30869/jtii.v4i2.391

Abstract

Tingginya beban listrik yang mencapai 325 MegaWatt, hal ini merupakan perhatian penting bagi pemerintah Provinsi Gorontalo dalam kebutuhan energi listrik, maka perlu memprediksi lama penyinaran matarahari pada suatu daerah, Energi sel surya salah satunya bergantung pada lamanya penyinaran cahaya matahari. Diantaranya dengan melakukan perancangan model prediksi. Metode prediksi yang mimiliki nilai error terkecil adalah Neural Network, akan tetapi masih adanya kelemahan pada waktu pelatihan untuk mencapai konvergen dan overfitting. Maka perlu dilakukan optimalisasi pada bobot jaringan dengan menggunakan Particle Swarm Optimazition, yang merupakan salah satu metode terbaik dalam optimasi. Dengan penggunaan optimasi yang diukur melalui hasil peroleha Root Mean Square Error (RMSE). Hasil pengujian terhadap algoritma menunjukkan bahwa nilai RMSE mengunakan Neural Network 0,131, sedangkan dengan penerapan optimasi dengan particle swarm optimization hasil RMSE 0,127. Dengan penerapan metode optimasi terserbut dapat mengurangi nilai error
Optimasi Data Rekam Medis Pasien Rawat Inap BPJS RSUD Tombulilato dengan Algoritma K-Means dan FP-Growth Fatiya Ointu; Alter Lasarudin; Wahyudin Hasyim
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 4 (2025): Agustus 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i4.9348

Abstract

Abstrak - Pengelolaan data rekam medis, terutama dalam meningkatkan kualitas layanan rumah sakit di era digital, menjadi komponen yang semakin penting. Penelitian ini bertujuan untuk mengoptimalkan pengelolaan data rekam medis pasien rawat inap BPJS di Rumah Sakit Umum Daerah Tombulilato dengan menerapkan algoritma K-Means untuk clustering dan analisis pola asosiasi menggunakan algoritma FP-Growth. Hasil penelitian menunjukkan bahwa Cluster 0 terdiri dari pasien perempuan di Kelas Rawat 3, berusia dewasa (18-64 tahun), yang mengalami penyakit infeksi (dukungan 16,4% dengan kepercayaan 100%), penyakit pernapasan (dukungan 7,1% dengan kepercayaan 100%), dan komplikasi kehamilan (dukungan 11,6% dengan kepercayaan 100%). Sementara itu, Cluster 1 terdiri dari pasien laki-laki di Kelas Rawat 3, berusia dewasa (18-64 tahun), yang mengalami penyakit infeksi (dukungan 13,6% dengan kepercayaan 100%), penyakit pernapasan (dukungan 16,2% dengan kepercayaan 100%), dan penyakit mental (dukungan 10,3% dengan kepercayaan 100%).Kata kunci: FP-Growth; K-Means; Rekam Medis; Abstract - It is becoming increasingly crucial to manage medical record data in order to enhance the quality of hospital services in the digital era. This study aims to optimize the management of medical record data for BPJS inpatients at Tombulilato Regional General Hospital by applying the K-Means algorithm for clustering and association pattern analysis using the FP-Growth algorithm. The results showed that Cluster 0 consisted of female patients in Class 3, adults (18-64 years), who experienced infectious diseases (16.4% support with 100% confidence), respiratory diseases (7.1% support with 100% confidence), and pregnancy complications (11.6% support with 100% confidence). Cluster 1 was composed of male patients in Class 3, adults aged 18-64 years, who had been diagnosed with infectious diseases (13.6% support with 100% confidence), respiratory diseases (16.2% support with 100% confidence), and mental illness (10.3% support with 100% confidence).Keywords: FP-Growth; Medical Records; K-Means;
Pemberdayaan UMKM Lokal Melalui Integrasi Website Desa sebagai Media Pemasaran Digital di Desa Pentadu Barat: Empowering Local MSME through the Integration of Village Website as a Digital Marketing Platform in Pentadu Barat Village Kartika, Dhian Satria Yudha; Sukma, Dini Artika; Lasarudin, Alter; Luawo, Salma Rivani
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 11 No. 1 (2026): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v11i1.10668

Abstract

The development of digital technology has created new opportunities for Micro, Small, and Medium Enterprises (MSMEs) to expand their market reach. However, in Pentadu Barat Village, MSME product promotion had previously relied on conventional methods, limiting the potential for local economic growth. Through the Community Service Program (KKN), MSMEs were integrated into the official village website, which features village profiles, MSME catalogs, articles and galleries, testimonials, and contact services. This study aims to describe the integration process and assess the role of the village website as a digital promotional medium. The research employed a qualitative descriptive approach, involving observation, data collection on business identity, and interviews with MSME actors and village officials. The findings indicate that the village website serves as an effective promotional medium, as reflected in increased visibility of local products, more structured digital documentation, and positive community responses. Nevertheless, the main challenge lies in sustainability, particularly regarding data updates and content management. With consistent management, the village website can function not only as a digital showcase but also as an instrument of community empowerment and local economic strengthening in the digital era.
Descriptive writing skills in the influencer era: sentiment effects of digital content Agus Salim Lamusu; Hasna Nurain Mukhsin; Alter Lasarudin
JPPI (Jurnal Penelitian Pendidikan Indonesia) Vol. 11 No. 4 (2025): JPPI (Jurnal Penelitian Pendidikan Indonesia)
Publisher : Indonesian Institute for Counseling, Education and Theraphy (IICET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29210/020256601

Abstract

This study investigates the influence of influencer-generated digital content on university students’ descriptive writing skills, with a focus on the role of sentiment and digital literacy. Employing a quantitative cross-sectional design, data were collected from 63 students at Universitas Muhammadiyah Gorontalo and IAIN Sultan Amai Gorontalo using questionnaires and semi-structured interviews. Students’ perceptions of writing quality were analyzed in relation to their exposure to digital content with positive, neutral, and negative sentiment. The findings reveal a significant correlation between exposure to positive-sentiment content and perceived improvements in creativity, vocabulary use, and idea organization, while neutral and negative content is associated with more formal but less expressive writing. The study also indicates that digital literacy mediates how students adapt informal digital language to academic writing demands. These results suggest that integrating digital literacy into writing instruction is essential to help students navigate the influence of digital media on academic writing.
Analisis Sentimen Terhadap Kebijakan Efisisensi Anggaran Pemerintah Menggunakan Metode Support Vector Machine (SVM) Dan Vector Space Model (VSM) Dwi Fanti Utina; Alter Lasarudin; Wahyudin Hasyim
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 2 (2026): April, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/twjsnn25

Abstract

Abstrak - Media sosial saat ini menjadi wadah utama bagi masyarakat dalam menyuarakan aspirasi terhadap kebijakan pemerintah secara terbuka, termasuk isu kebijakan efisiensi anggaran. Penelitian ini bertujuan menganalisis kecenderungan sentimen publik terhadap kebijakan pemerintah menggunakan algoritma Support Vector Machine (SVM) yang diintegrasikan dengan Vector Space Model (VSM). Data dikumpulkan melalui teknik crawling pada platform X (Twitter), kemudian diproses melalui tahapan cleaning, case folding, tokenizing, filtering, dan stemming. Penelitian ini juga menerapkan pendekatan Lexicon-Based untuk pelabelan sentimen. Seluruh implementasi sistem dikembangkan menggunakan bahasa Python pada Google Colaboratory. Hasil pengujian dengan confusion matrix menunjukkan bahwa model mampu mengklasifikasikan data berlabel mandiri dengan akurasi 71%, dan mencapai 96% pada data berlabel Lexicon-Based Approach. Hal ini membuktikan integrasi VSM sangat membantu SVM dalam menyajikan representasi fitur dan mengenali pola teks secara akurat. Penelitian ini menyimpulkan bahwa persepsi masyarakat dapat dipetakan secara objektif melalui analisis sentimen, sehingga dapat menjadi bahan evaluasi dan referensi strategis bagi pemerintah dalam mengambil kebijakan di masa depan. Kata Kunci: Analisis Sentimen; Support Vector Machine; Efisiensi Anggaran; X (Twitter); Vector Space Model;   Abstract - Social media is currently the primary platform for the public to openly voice their concerns about government policies, including issues of budget efficiency. This study aims to analyze public sentiment trends toward government policies using the Support Vector Machine (SVM) algorithm integrated with the Vector Space Model (VSM). Data was collected through crawling techniques on the X (Twitter) platform, then processed through cleaning, case folding, tokenizing, filtering, and stemming. This study also applied a Lexicon-Based approach for sentiment labeling. The entire system implementation was developed using Python on Google Collaboratory. Test results using a confusion matrix showed that the model was able to classify self-labeled data with 71% accuracy and achieved 96% accuracy on data labeled with the Lexicon-Based Approach. This demonstrates that the VSM integration significantly assists the SVM in presenting feature representations and accurately recognizing text patterns. This study concludes that public perception can be objectively mapped through sentiment analysis, thus serving as evaluation material and a strategic reference for the government in future policymaking.                                                                                                     Keywords: Sentiment Analysis; Support Vector Machine; Budget Efficiency; X (Twitter); Vector Space Model;
Penerapan Algoritma Extrame Gradient Boosting (XGBoost) untuk Menganalisis Kualitas Pelayanan Kesehatan Terhadap Kepuasan Pasien di Pueskesmas Mootilango Rindiyani Saputri Agu; Wahyudin Hasyim; Alter Lasarudin
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 2 (2026): April, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/g69g4z25

Abstract

Abstrak - Pengelolaan data kepuasan pasien menjadi komponens yang semakin penting dalam meningkatkan kualitas pelayanan kesehatan di era digital. Penelitian ini bertujuan untuk mengklasifikasikan tingkat kepuasan pasien di Puskesmas Mootilango dengan menerapkan algoritma Extreme Gradient Boosting (XGBoost) berbasis dimensi SERVQUAL. Tahapan penelitian dimulai dari pengumpulan data kuesioner, preprocessing, hingga implementasi model melalui optimasi Gradient dan Hessian. Transparansi logika algoritma divalidasi secara manual melalui perhitungan nilai Similarity Score dan Gain. Hasil penelitian menunjukkan bahwa model XGBoost memiliki performa andal dengan tingkat akurasi sebesar 80,00% dan recall sebesar 88%. Analisis Feature Importance mengidentifikasi bahwa indikator Q5 (Tata Letak Ruangan) dari dimensi Tangibles dan Q8 (Keakuratan Diagnosa) dari dimensi Reliability merupakan faktor paling berpengaruh terhadap kepuasan pasien. Simulasi manual pada indikator Q5 menghasilkan nilai Total Gain sebesar 0,7238, yang membuktikan kontribusi signifikan fitur tersebut dalam meningkatkan kemurnian klasifikasi. Disimpulkan bahwa algoritma XGBoost efektif memberikan rekomendasi strategis bagi manajemen untuk memprioritaskan perbaikan pada aspek fisik dan keandalan medis demi optimalisasi kualitas pelayanan. Kata kunci : Kepuasan Pasien; XGBoost; SERVQUAL; Machine Learning; Feature Importance;   Abstract -Patient satisfaction data management is becoming an increasingly important component in improving the quality of healthcare services in the digital era. This study aims to classify patient satisfaction levels at the Mootilango Community Health Center by applying the Extreme Gradient Boosting (XGBoost) algorithm based on the SERVQUAL dimension. The research stages start from questionnaire data collection, preprocessing, to model implementation through Gradient and Hessian optimization. The transparency of the algorithm logic is manually validated by calculating the Similarity Score and Gain values. The results show that the XGBoost model has reliable performance with an accuracy rate of 80.00% and a recall of 88%. Feature Importance analysis identified that indicator Q5 (Room Layout) from the Tangibles dimension and Q8 (Diagnostic Accuracy) from the Reliability dimension are the most influential factors on patient satisfaction. Manual simulation on indicator Q5 produced a Total Gain value of 0.7238, which proves the significant contribution of this feature in improving the purity of the classification. It was concluded that the XGBoost algorithm effectively provides strategic recommendations for management to prioritize improvements in physical aspects and medical reliability to optimize service quality. Keywords: Patient Satisfaction; XGBoost; SERVQUAL; Machine Learning; Feature Importance;
Co-Authors Agus Salim Lamusu Antupetu, Rastin Arifin Ahudulu Bagus Setio Raharjo Bakari, Sitti Nurfatimah Citra Ayu Dhian Satria Yudha Kartika Dian Hidayat Rahmola Djainab Humolungo Dwi Fanti Utina Fahriyadi Purnama Thaib Fatiya Ointu Fatmawati Meilan Harun Frangky Tupamahu Gani, Hilmansyah Habie, Khairul Fathan Hadi Pratomo Handayani, Tri Pratiwi Hapsa Husain Hasanah, Ika Nur Hasna Nurain Mukhsin Ibrahim, Irawan Ivone Djafar Larote, Yonis Luawo, Salma Rivani Miranda A. Hasan Misran Tomayahu Mohamad Akmal Djeden Mohamad Ilyas Abas N Dian Hidayat Rahmola Natalia R Simon Noviyanti Patak Lembang Olii, Muhammad Ramdhan Polapa, Risman Purwanto Purwanto Rasmawati Ika Rindiyani Saputri Agu Rizal Lamusu Roy Dumako Roy Dumako Rubiyanto Maku Siti Juliana Pakaya Sitnasanti Yane Stevie Eka Putri Lamato Sukma, Dini Artika SYAHRIAL SYAHRIAL Terri Repi Tri Pratiwi Handayani Wah Ha Wahyudin Hasyim Widya Kurniati Yulanda Yunus Yusuf Lalu