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Anam, M Khairul

Universitas Samudra

Author-ID : 1573830

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DIGITALISASI LAYANAN PERPUSTAKAAN: PENGEMBANGAN KATALOG BUKU BERBASIS WEB PADA DINAS PERPUSTAKAAN DAN KEARSIPAN KOTA LANGSA Siregar, Ginda Maruli Andi; M. Khairul Anam; Ahmad Ihsan; Liza Fitria; Munawir; Khairul Muttaqin
Jurnal Masyarakat Berdikari dan Berkarya (Mardika) Vol 2 No 4 (2024): Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA)
Publisher : Fakultas Teknik, Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/mardika.v2i4.12511

Pengelolaan informasi buku yang efektif dan efisien menjadi kebutuhan penting dalam pelayanan perpustakaan modern. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk membantu Dinas Perpustakaan dan Kearsipan Kota Langsa dalam meningkatkan kualitas layanan melalui implementasi Sistem Informasi Katalog Buku berbasis digital. Sistem ini dirancang untuk memudahkan pengguna dalam mencari, melihat, dan mengakses informasi buku secara cepat, serta memfasilitasi admin dalam mengelola data buku dan data admin secara terstruktur. Sistem terdiri dari dua entitas utama, yaitu user dan admin, dengan fitur yang mencakup pencarian katalog, manajemen data buku, dan pengelolaan akun admin. Hasil implementasi menunjukkan bahwa sistem ini mampu meningkatkan efisiensi pengelolaan data buku serta mempermudah akses informasi bagi masyarakat. Diharapkan sistem ini dapat terus dikembangkan dengan fitur yang lebih inovatif dan mendukung transformasi digital layanan perpustakaan.

Utilization of the e-Polvot System to Increase Student Participation in Higher Education Anam, M. Khairul; Zoromi, Fransiskus; Hamdani, Hamdani; Efendi, Yoyon; Kudadiri, Parlindungan
JTERA (Jurnal Teknologi Rekayasa) Vol 10, No 1: Juni 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i1.2025.165-172

STMIK Amik Riau currently has a student organization, one of which is the Student Executive Board (BEM). Every year, BEM is chosen as the representative of the students to voice their aspirations both within and outside the campus. The election process for BEM representatives is still conducted traditionally on campus. However, not all students can participate in the election process regularly, resulting in some students being unable to vote for the candidates for the student president and vice-president. To address this issue, a system is needed to facilitate the election process. One solution is to conduct electronic elections, allowing all students to vote from anywhere using the internet. In this research, the developed system is called e-Polvot (Electronic Polling and Voting). This system can be used by anyone to conduct elections by creating their own election campaigns. Before designing the system, the research conducted a measurement of the potential users using the TRI model. The results from the TRI measurement were then used to determine whether the system should be developed or not. Subsequently, the e-Polvot system was built and implemented at STMIK Amik Riau. After the implementation, the system underwent black box testing to ensure its functionality. The testing results showed that the main features of the e-Polvot system were able to perform well.

The Application of Na ve Bayes Classifier Based Feature Selection on Analysis of Online Learning Sentiment in Online Media Putra, Ryanda Satria; Agustin, Wirta; Anam, M. Khairul; Lusiana, Lusiana; Yaakub, Saleh
Jurnal Transformatika Vol. 20 No. 1 (2022): July 2022
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i1.5144

There are problems that still exist in online learning including limited-reach networks, inadequate facilities and infrastructure, and others. This study discussed the analysis of sentiment which used the Na ve Bayes Classifier (NBC) method with XGBoost feature selection as a performance improvement that took data from news portals. The results of this study showed that graph data on the application of online learning forms in Indonesia had a "Negative" opinion. Performance testing of the NBC method based on XGBoost feature selection was conducted four times. The first experiment resulted in an accuracy value of 60.18% with 50/50 split data. The next experiment had an accuracy value of 56.92% with 70/30 split data. After that, the third experiment resulted in an accuracy value of 65.90% with 80/20 split data. The result of the last experiment was an accuracy value of 63.63% with 90/10 split data. After using XGBoost feature selection, it produced an accuracy of 60.18%, 67.69%, 70.45%, and 77.27%. The study also produced the highest average score at 10-Fold Cross-Validation in the second trial with a score of 65.62%.

Sentiment Analysis Optimization Using Ensemble of Multiple SVM Kernel Functions M. Khairul Anam; Lestari, Tri Putri; Efrizoni, Lusiana; Handayani, Nadya Satya; Andhika, Imam
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6708

This research aims to optimize sentiment analysis by leveraging the strengths of multiple Support Vector Machine (SVM) kernels—Linear, RBF, Polynomial, and Sigmoid—through an ensemble learning approach. This study introduces a novel model called SVM Porlis, which integrates these kernels using both hard and soft voting strategies to improve the classification performance on imbalanced datasets. Sentiment classification in this study involves two classes: positive and negative. Tweets related to the controversy over the naturalization of Indonesian national football players were collected using the official X/Twitter API, resulting in a dataset of 2,248 entries. The dataset was notably imbalanced, with significantly more negative samples than positive samples. Data preprocessing included cleaning, labeling, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. To address the class imbalance, the SMOTE technique was applied to synthetically augment the minority class. Each SVM kernel was trained and evaluated individually before being combined into an SVM Porlis model. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix analysis. The results demonstrate that SVM Porlis with soft voting achieved the highest performance, with 98% accuracy, precision, recall, and F1-score, surpassing the performance of individual kernels and other ensemble approaches such as SVM + Chi-Square and SVM + PSO. These findings highlight the effectiveness of combining multiple kernels to capture both linear and non-linear patterns, offering a robust and adaptive solution for sentiment analysis in real-world, imbalanced data scenarios.

Klasifikasi Emosi Terhadap Konflik Israel-Palestina Menggunakan Algoritma Gated Recurrent Unit Saputra, Eko Ikhwan; Fatdha, T.Sy. Eiva; Agustin; Junadhi; M. Khairul Anam
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4106

The Israel-Palestine conflict intensified following the October 7, 2023, attack by Hamas on Israel, triggering various emotional reactions on social media. Emotion classification is crucial for understanding public sentiment related to this conflict. This study utilizes 9,917 tweets from platform X (Twitter) to classify emotions such as joy, sadness, anger, fear, disgust, and surprise. The deep learning algorithm used is Gated Recurrent Unit (GRU), developed with three different training and testing data splits: 70:30, 80:20, and 90:10. For text representation, Global Vector (GloVe) word embedding is employed. Given the imbalanced dataset, this study applies the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. The research results indicate that the GRU model with a 90:10 data split without using SMOTE achieves the highest accuracy of 75%, followed by the models with 70:30 and 80:20 splits, which each have an accuracy of 73%.

OPTIMASI TEKNIK VOTING PADA SENTIMEN ANALISIS PEMILIHAN PRESIDEN 2024 MENGGUNAKAN MACHINE LEARNING Kharisma Rahayu; M. Khairul Anam; Lusiana Efrizoni; Nurjayadi; Triyani Arita Fitri
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4119

The presidential election is an important event in the democratic system of the Unitary State of the Republic of Indonesia or NKRI held every five years. There are many pros and cons of the presidential election, especially on social media Twitter or X. X is one of the media platforms where people leave positive, neutral, and even negative comments. Therefore, this research aims to build a sentiment analysis model to classify the sentiment of the 2024 presidential election. This research uses the Support Vector machine, Naïve Bayes and Decision Tree algorithms in performing classification with the addition of the Syntethic Minority Over-Sampling and Ensemble Voting methods. The test results show that public sentiment towards the presidential election dominates negative sentiment of 5008 positive 3582 and neutral 1411 sentiments. Then the results of data processing SVM, NB and DT algorithms plus SMOTE and ensemble voting optimization, provide 92.8% accuracy, 93% precision, 93% recall and 93% F1-Score. This research can make a significant contribution by classifying public sentiment towards the 2024 presidential election data.

IMPLEMENTASI SISTEM INFORMASI AKADEMIK DI PKBM AR ROYYAN UNTUK MENINGKATKAN EFISIENSI ADMINISTRASI DAN MONITORING Yuda Irawan; Refni Wahyuni; Abdi Muhaimin; M. Khairul Anam
Jurnal Masyarakat Berdikari dan Berkarya (Mardika) Vol 3 No 2 (2025): Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA)
Publisher : Fakultas Teknik, Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/mardika.v3i2.12965

PKBM Ar Royyan menghadapi tantangan dalam pengelolaan data akademik seiring dengan peningkatan jumlah siswa dan kompleksitas administrasi. Pengelolaan data yang masih dilakukan secara manual menyebabkan inefisiensi, risiko kehilangan data, serta keterbatasan sumber daya manusia. Oleh karena itu, adopsi sistem informasi akademik digital menjadi sangat penting untuk mengatasi masalah ini. Tujuan dari kegiatan pengabdian masyarakat ini adalah meningkatkan efisiensi pengelolaan data akademik dan administrasi di PKBM Ar Royyan melalui implementasi Sistem Informasi Akademik (SIAKAD). Selain itu, kegiatan ini bertujuan menjamin keamanan dan keberlanjutan data, serta meningkatkan kapasitas teknologi informasi di sekolah melalui pelatihan penggunaan sistem digital. Hasil kegiatan menunjukkan bahwa penerapan SIAKAD secara signifikan berhasil meningkatkan efisiensi pengelolaan data dan mempercepat proses administratif. Meskipun terdapat tantangan infrastruktur dan keterampilan teknologi, masalah tersebut berhasil diatasi melalui pelatihan dan dukungan teknis. Implementasi ini juga mendorong transparansi dan memberikan contoh bagi sekolah lain di wilayah tersebut dalam memanfaatkan teknologi untuk meningkatkan kualitas pendidikan. Secara keseluruhan, kegiatan ini membangun fondasi bagi pengembangan teknologi pendidikan yang berkelanjutan di masa depan.

OPTIMALISASI ALGORITMA SUPPORT VECTOR MACHINE PADA ASPECT-BASED SENTIMENT ANALYSIS MENGGUNAKAN GRIDSEARCHCV Saputra, Eko Ikhwan; Anam, M. Khairul; Yenni, Heda; Hamdani, Hamdani; Zamsuri, Ahmad
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 1 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v7i1.17800

Support Vector Machine (SVM) merupakan salah satu algoritma Machine Learning yang umum digunakan untuk menyelesaikan permasalahan klasifikasi. SVM sangat baik digunakan untuk klasifikasi biner, tetapi masih kurang optimal dalam melakukan klasifikasi multi-class. SVM masih mendapatkan akurasi yang rendah dalam melakukan klasifikasi multi-class, rentang akurasi yang didapatkan sekitar 52% - 62%. Ketidak konsistenam SVM dalam melakukan klasifikasi multi-class perlu dilakukan perbaruan agar lebih baik lagi. Penelitian ini menggunakan dataset opini masyarakat tentang Pariwisata Yogyakarta, label dari dataset adalah representasi dari wisatawan. Jumlah data wal yang diperoleh adalah 4121 baris, memiliki 5 label dalam hal ini pelabelan Aspect-Based Sentiment Analysis. Penelitian ini memiliki tahpaan data acuisition, pre-processing data, feature extraction, feature selection, modelling dan evaluasi. Penelitian ini melakukan percobaan pada 2 kernel SVM yang berbeda, yaitu linear dan rbf. Kemudian dilakukan hyperparameter tuning menggunakan GridSearchCV untuk mendapatkan parameter terbaik dari algoritma SVM. GridSearchCV dapat meningatkan akurasi SVM dengan kernel linear dengan nilai peningkatan tertinggi nya mencapai 7%. Akurasi pada kernel Rbf lebih tinggi yaitu 67,4% setelah dilakukan hyperparameter tuning.

ANALISIS KESIAPAN SEKOLAH MENENGAH DALAM MENERAPKAN E-VOTING MENGGUNAKAN MODEL TECHNOLOGY READINESS INDEX Hazira, Nadila; Anam, M. Khairul; Agustin, Wirta; Fitri, Triyani Arita; Zamsuri, Ahmad; Syam, Salmaini Safitri
ZONAsi: Jurnal Sistem Informasi Vol. 6 No. 2 (2024): Publikasi Artikel ZONAsi: Periode Mei 2024
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v6i2.18400

Voting can be interpreted as a way of making decisions based on the largest number of votes. So far, voting is carried out by ticking or voting on a ballot paper as an option in holding the election for OSIS chairman at SMAN 15 Pekanbaru. This method is considered still very conventional amidst advances in technology and information which has weaknesses in terms of efficiency and effectiveness. The weaknesses of conventional voting are: the decision is not the result of consensus, some participants are forced to accept the decision that has been taken, some participants often do not accept the decision, the aspirations of the participants are not fully channeled. To reduce problems arising from manual voting, it is necessary to analyze the readiness of secondary schools in implementing e-voting using the Technology Readiness Index model. The method that can be used to measure the level of user readiness in using technology is the Technology Readiness Index (TRI). In order to find out the results of the analysis and test the readiness of secondary schools in implementing the new system, the author will conduct a survey by distributing a Google Form link containing a list of statements regarding the readiness of secondary school residents, especially at SMAN 15 Pekanbaru, in using the web-based E-Voting system for the election of chairman. Student Council. The survey results will be analyzed using the SPSS 25.0 application and also calculated using the Technology Readiness Index Method

Optimization of Machine Learning Models in Student Graduation Prediction Systems Using Ensemble Learning with PSO and SMOTE Hamdani, Hamdani; Susanti, Susanti; Lathifah, Lathifah; Anam, M. Khairul; Pradipta, Rahman
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15335

The timely graduation of students is a key metric in evaluating the academic effectiveness of higher education institutions. However, accurately identifying students at risk of delayed graduation remains challenging due to imbalanced data distributions and the instability of single-model prediction approaches. This study proposes an optimized ensemble-based machine learning system for predicting on-time graduation among university students. The model integrates C4.5, K-Nearest Neighbor (KNN), and Random Forest algorithms through a hard voting classifier, which is further optimized using Particle Swarm Optimization (PSO) to determine the most effective weighting configuration. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is implemented, ensuring balanced representation between timely and delayed graduates. A dataset of 809 student academic records from Universitas Sains dan Teknologi Indonesia (USTI) was used, and performance was evaluated using 5-fold cross-validation. The proposed ensemble model achieved an average accuracy of 93.70%, a precision of 0.94, a recall of 0.93, and an F1-score of 0.94, outperforming each individual classifier. These results confirm that the combination of ensemble learning, PSO-based optimization, and data balancing effectively improves both accuracy and model stability. The findings highlight the system’s potential as a reliable decision-support tool for educational institutions to anticipate delayed graduations and improve academic supervision strategies.

Co-Authors -, Tashid Abrar Hadi Ade Riyanda Putra Agustin Agustin Agustin Agustin Agustin Agustin Agusviyanda Agusviyanda Agusviyanda Agusviyanda Ahmad Ihsan Ahmad Zamsuri Ahmad Zamsuri, Ahmad Aisum Aliyah Sari Akram, Rizalul Al Amin Fadillah Sani Alfa Saleh Alfisyahrin, Alfisyahrin Alfitra, TM Rezaka Alkadri Masnur Ambiyar, Ambiyar Andesa, Khusaeri Andhika, Imam Andi Supriadi Chan, Andi Supriadi Anwar, Reksi Aprillian Kartino Arba, Muhammad Hendra Arda Yunianta Arda Yunianta Arief Hidayat Arita Fitri, Triyani Arsyah, Ulya Ilhami Atalya Kurnia Sari Atmaja, Teuku Hadi Wibowo Bambang Kurniawan Br.Situmorang, Elisabet Sinta Romaito Budiman, Edy Budiman, Edy Bunga Nanti Pikir Bunga Nanti Pikir Chatarina Umbul Wahyuni Cut, Banta Damar Sanggara Habibie Darma, Adi Surya Daryanto, Diki Dea Safitri Dedy Irfan Devi Yuliana Dewi Sari Wahyuni Didik Sudyana Didik Sudyana Diki Daryanto Diky Daryanto Dona Wahyuning Laily Eddy Kurniawan Pradana Efrizoni, Lusiana Elangga Sony Widiharsono Elva, Yesri Emerlada, Esi Tri Erlin Erlin Erlinda, Susi Ersan Fadrial, Yogi Esi Tri Emerlada Fadli Suandi Fahrul Yamani Faisol Mas’ud Fajar Arifandi Fajrizal Fatdha, T.Sy. Eiva Faza Alameka Fernando Elda Pati Fika Felanda Ardelia Firdaus, Muhammad Bambang Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Fryonanda, Harfebi Fuquh Rahmat Shaleh Gendhy Dwi Harlyan Gubtha Mahendra Putra Gunadi Gunanti Mahasri Gunawan, Chichi Rizka Habibi Ulayya Hadi Asnal, Hadi Hairah, Ummul Halim, Muhammad Yusuf Hamdani Hamdani - Hamdani . Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Handayani, Nadya Satya Hanif Aulia Happy Yugo Prasetiya Haris Kurniawan, Haris Harja, Jetno Hartomi, Zupri Henra Hasan J. Alyamani Haviluddin Haviluddin Hazira, Nadila Helda Yeni Helda Yenni, Helda Hendra Saputra Hendrawan, Riki hendri, nofri Herianto Herianto Herwin Herwin Ika Purnamasari Ike Yunia Pasa Ikhsan Ikhsan Indah Mukhlis Tamara Indra Prayogo Indra Prayogo Indri Febrianti Irfan Putra Pratama Irfansyah Irfansyah Irfansyah Irfansyah Irsyad, Akhmad Irwanda Syahputra Irwanda Syahputra Irzal Arief Wisky Istianah Istianah Jamaris, Muhamad Jamaris, Muhammad Jasmarizal Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Kadek Mirnawati Karfindo, Karfindo Karpen Kartina Diah K. W. Kharisma Rahayu Khusaeri Andesa Khusaeri Andesa Kresnapati, I Nyoman Bagus Aji Kudadiri, Parlindungan Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah, Lathifah Latifah Lia Oktavia Ika Putri Lilis Cahaya Septiana Liza Fitria Lucky Lhaura Van FC Lucky Lhaura Van FC, Lucky Lhaura Lusiana Lusiana Efrizoni Lusiana Lusiana M Syauqi Hafizh M. Ikhsan Wibowo Machdalena Mahamad, Abd Kadir Mahendra, Muhammad Ihza Mahessya, Raja Ayu Mardainis Mardainis Mardainis Martilinda Panjaitan Mega Susanti Mega Susanti Melda Royani Michal Dennis Michel Kasaf Mi`rajul Rifqi Mohamad, Nur Ikhwan Bin Muhaimin, Abdi Muhamad Jamaris Muhamad Sadar Muhamad Sadar, Muhamad Muhammad Bambang F Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Budi Saputra muhammad Fuad Muhammad Ikhsan Wibowo Muhammad Nur Ihwan Muhammad Wisdan Pratama Putra Munawir Munawir Munawir N.A, Randi Nadila Rahmadhani Nadya Alinda Rahmi Nanda, Novianda Nanda Nariza Wanti Wulan Sari Nasrul Sani Neci Nirwanda Nina Nurmalia Dewi Nisa, Aida Nora Lizarti Novi Yona Sidratul Munti Nu'man, Nu'man Nurhuda, Agus Tri Nurjayadi Nurjayadi Nurjayadi Nurjayadi Nurjayadi Nurjayadi Nurkholifah Dwi Rahayu Nurul fadillah, Nurul Nurul Indriani Nurwijayanti Pandu Pratama Putra, Pandu Pratama Paradila, Dinda Parlindungan Kudadiri Permana, Randy Pradipta , Rahman Pranata, Angga Pratiwi, Mutiana Purwanto Putra, Ryanda Satria Rahmaddeni Rahmaddeni Rahmaddeni Rahmaddeni Rahmi, Nadya Alinda Rahmiati Rahmiati Rahmiati Rebecca La Volla Nyoto Refni Wahyuni Reksi Anwar Rini Yanti Rini Yanti Rini Yanti Rinno Hendika Putra Rio Andika Malik Rivaldi Dwi Andhika Rohana Yola Parastika Hutasoit Rohmat Romadhoni Rometdo Muzawi, Rometdo Ruri Hartika Zain Saiful Bukhori Salman Aldo Alfaresi Salsabila Rabbani Salsabila Rabbani Saon, Sharifah Saputra, Eko Ikhwan Sari Irma Yani Sitorus Sari, Atalya Kurnia Sarjon Defit Setyantini, Woro Hastuti Silvyana Dwi Putri Sofiansyah Fadli Sofiansyah Fadli Soni Sovia, Rini suaidah suaidah Sumijan Sumijan Susandri, Susandri Susanti Susanti Susanti Susanti Susanti Susanti Susanti, Mega Susanti, Susanti Susi Erlinda Susi Erlinda SUSI ERLINDA Syam, Salmaini Safitri Syamsiar, Syamsiar T. Sy. Eiva Fatdha Taruk, Medi Tashid Tashid Tashid Tatang Hidayat Taufik Taufik Tejawati, Andi Tengku Alvin Firdaus Teri Ade Putra Tjut Rizqi Maysyarah Hadi Torkis Nasution Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari, Tri Putri Triyani Arita Fitri Ulfah, Aniq Noviciate Wahyudianto, Mochamad Rizky Wahyuni, Dewi Sari Waksito, Alan Zulfikar Waskita, Ghozi Indra Wifra, Rizki Wirta Agustin Wirta Agustin Yaakub, Saleh Yansyah Saputra Wijaya Yenni, Heda Yesaya Twin Situmorang Yogi Ersan Fadrial Yogi Yunefri, Yogi Yoyon Efendi Yuda Irawan Yudhistira, Dewangga Yumami, Eva Zainal Arifin Zeki Kurniadi zeki Kurniadi

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