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Nurdin Nurdin
Universitas Malikussaleh
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Sistem Pendukung Keputusan Rekomendasi Paket IndiHome Menggunakan Metode Multi-Attribute Utility Theory (MAUT), Studi Kasus: PT. Telkom Persero Bireuen M Raisal Al Farisi; Nurdin Nurdin; Muhammad Sayuti
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10007

Abstract

IndiHome merupakan layanan internet yang menawarkan berbagai pilihan paket kepada pelanggannya, mencakup layanan internet, TV kabel, dan telepon. Beragamnya pilihan paket sering kali membingungkan pelanggan dalam memilih paket yang sesuai dengan kebutuhan mereka. Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan (SPK) yang dapat memberikan rekomendasi paket IndiHome terbaik untuk calon pelanggan berdasarkan kriteria tertentu menggunakan metode Multi-Attribute Utility Theory (MAUT). Sistem ini dibangun menggunakan framework Laravel dan database MySQL. Data yang digunakan dalam penelitian ini sebanyak 100 dataset pelanggan PT. Telkom Persero Bireuen. Atribut penilaian yang diintegrasikan meliputi kebutuhan pelanggan , kecepatan internet , jumlah perangkat , pendapatan pelanggan , dan pengeluaran pelanggan . Hasil perhitungan akhir menunjukkan alternatif BJ_45 memperoleh nilai preferensi tertinggi sebesar 0.8896, sehingga direkomendasikan sebagai prioritas utama penawaran paket layanan. Pengujian fungsional menggunakan black-box testing menunjukkan bahwa sistem mampu memberikan hasil rekomendasi yang akurat dan sesuai dengan preferensi calon pelanggan.
ANALISIS KETANGGUHAN ALGORITMA META PROPHET DALAM PERAMALAN DERET WAKTU HARGA EMAS PADA KONDISI FLUKTUATIF Khalis Al Muqarrabin; Nurdin Nurdin; M Sayuti
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10022

Abstract

Peramalan data deret waktu pada sektor finansial, khususnya instrumen harga emas, sering kali mengalami penurunan tingkat akurasi yang sangat drastis ketika terjadi guncangan makroekonomi yang memicu volatilitas ekstrem di bursa. Mayoritas algoritma peramalan konvensional gagal beradaptasi dengan cepat terhadap anomali data dan pergeseran tren secara tajam, yang pada akhirnya dapat menghasilkan rekomendasi keliru pada sistem pendukung keputusan investasi. Penelitian ini bertujuan untuk mengevaluasi tingkat ketangguhan algoritma machine learning modern, yaitu Meta Prophet, dalam meramalkan harga emas Antam harian. Metode yang diusulkan menyegmentasi dataset historis periode 2010 hingga 2026 ke dalam tiga fase pengujian yang terisolasi secara ketat: fase normal (2019), fase krisis pandemi (2020–2022), dan fase pemulihan ekonomi (2023–2026). Hasil eksperimen membuktikan bahwa algoritma Meta Prophet secara signifikan sangat adaptif pada kondisi pasar yang bergejolak. Melintasi fase krisis dan pemulihan, Prophet mampu secara konsisten mempertahankan nilai Mean Absolute Percentage Error (MAPE) keseluruhan yang rendah sebesar 13,90%. Kesimpulan penelitian ini menunjukkan bahwa arsitektur Meta Prophet terbukti secara empiris tangguh terhadap pergeseran tren. Metode ini sangat direkomendasikan sebagai mesin prediktif utama untuk mengoptimalkan sistem pendukung keputusan di masa depan.
RANCANG BANGUN APLIKASI DIAGNOSIS GANGGUAN KESEHATAN MENTAL BERBASIS ANDROID MENGGUNAKAN FORWARD CHAINING Aura Munadila
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10290

Abstract

Kesehatan mental berperan penting dalam kesejahteraan individu, namun tingginya prevalensi gangguannya menuntut solusi teknologi yang mudah diakses. Penelitian ini mengembangkan sistem pakar berbasis Android menggunakan metode Forward Chaining untuk melakukan skrining awal terhadap lima gangguan kesehatan mental, yaitu kecemasan, serangan panik, PTSD, skizofrenia, dan OCD. Basis pengetahuan sistem dibangun berdasarkan 32 gejala yang diperoleh melalui studi literatur dan konsultasi dengan pakar di bidang kesehatan mental. Sistem dikembangkan menggunakan framework Flutter sehingga dapat berjalan pada platform Android secara efisien. Pengujian dilakukan melalui tiga metode, yaitu black-box testing, white-box testing, dan Test With Known Cases, yang menunjukkan bahwa seluruh fungsi sistem berjalan dengan baik dan tingkat kesesuaian hasil diagnosis mencapai 85,71%. Hasil penelitian ini membuktikan bahwa metode Forward Chaining efektif diterapkan dalam sistem pakar untuk skrining awal gangguan kesehatan mental, sehingga dapat mendukung masyarakat dalam mengenali gejala awal sebelum berkonsultasi dengan tenaga kesehatan mental yang kompeten
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI KROM BANK DIGITAL MENGGUNAKAN NAÏVE BAYES, LOGISTIC REGRESSION, DAN SUPPORT VECTOR MACHINE Aldi Wahdana; Nurdin Nurdin; Muhammad Sayuti
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10354

Abstract

Perkembangan teknologi informasi mendorong meningkatnya penggunaan aplikasi perbankan digital yang menghasilkan banyak ulasan pengguna pada Google Play Store. Ulasan tersebut penting untuk evaluasi layanan, namun jumlahnya yang besar menyulitkan analisis manual sehingga diperlukan pendekatan text mining otomatis. Penelitian ini bertujuan menganalisis sentimen ulasan pengguna aplikasi Krom Bank Digital serta membandingkan kinerja algoritma Naive Bayes, Logistic Regression, dan Support Vector Machine (SVM). Dataset yang digunakan berjumlah 11.718 ulasan, dan setelah proses pelabelan serta penghapusan data netral diperoleh 11.463 data. Tahapan penelitian meliputi preprocessing teks, ekstraksi fitur menggunakan TF-IDF, dan proses klasifikasi. Hasil penelitian menunjukkan bahwa SVM memberikan performa terbaik dengan akurasi 95,60% dan cross validation 95,21%, dibandingkan dua algoritma lainnya. Dengan demikian, SVM menjadi model paling optimal dalam klasifikasi sentimen. Penelitian ini diharapkan dapat membantu pengembang aplikasi dalam meningkatkan kualitas layanan serta memberikan gambaran mengenai opini pengguna melalui analisis data ulasan berbasis machine learning.
News Popularity Prediction in West Sumatera Using Autoregressive Integrated Moving Average Ansharulhaq Aminsyah; Nurdin Nurdin; Zara Yunizar
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.v5i1.615

Abstract

The increasing public interest in reading online news is undoubtedly a challenge for news portals as online news providers. Therefore, this research was conducted to predict news popularity in West Sumatra through the FajarSumbar.com news portal using the Autoregressive Integrated Moving Average (ARIMA) model. This research aims to develop a forecasting model that can assist in estimating the popularity of each news category so that news portals can devise more effective content strategies. The data used in this study includes the number of monthly news impressions from March 2021 to June 2024, which are grouped into various categories such as Religion Culture, Industrial Economics, Criminal Law, etc. Using the ARIMA method, which can handle time series data and overcome data non-stationarity problems through differencing and the use of grid search in optimization to find the best parameters based on the lowest evaluation metric. The results show that the ARIMA model can provide reasonably accurate predictions, although the level of accuracy varies between categories. The Mean Absolute Percentage Error (MAPE) values obtained are as follows: Religion Culture 26%, Industrial Economy 29%, Criminal Law 29%, Health 40%, Sports 38%, Tourism Entertainment 26%, Education 27%, Government Politics 31%, Social Environment 27%, and Technology 51%. The Technology and Health news categories show higher error rates than others, while Religion Culture and Tourism Entertainment have better accuracy rates. Thus, the ARIMA model can be used to predict future trends in news popularity, helping editors plan content strategies that are more relevant and interesting to readers. However, improvements are needed for news categories that have high variability.
Comparative Analysis of K-Means and K-Medoids to Determine Study Programs Salamah Salamah; Dahlan Abdullah; Nurdin Nurdin
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.v5i1.673

Abstract

Education is the main foundation for the advancement of civilization. A high level of education in society is directly proportional to the progress of that civilization. Higher education plays an important role in shaping quality human resources and contributing to community and national development. In today’s era of information and technology, data processing and analysis are key to understanding the development of study programs in higher education institutions. Clustering techniques are used to identify patterns and relationships in large and complex datasets, which are crucial in determining study programs at educational institutions. This research compares two popular clustering methods, K-Means and K-Medoids to determine study programs. The data used consists of odd semester grades of 87 students in the third-years of high school with 5 variables. The information of clusters is based on the minimum academic criteria of 18 study programs representing 7 faculties in Malikussaleh University and grouped into 5 clusters. The evaluation of clusters is conducted using the Davies-Bouldin Index (DBI). The result of the study indicate that K-Means algorithm has 5 clusters with cluster members of 31, 5, 13, 26 and 17, and a DBI value of 1,19010. Meanwhile, the K-Medoids algorithm has 5 clusters with cluster members of 33, 15, 17, 17 and 5, and a DBI value of 1,27833. Based on the DBI value, the K-Means algorithm demonstrates better cluster quality compared to the K-Medoids algorithm.
Performance Analysis of SVM and Linear Regression for Predicting Tourist Visits in North Sumatera Andriyan Ginting; Nurdin Nurdin; Cut Agusniar
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.v5i1.667

Abstract

Indonesia, an archipelago rich in cultural diversity, historical heritage, and stunning natural scenery, offers an extraordinary travel experience to visitors who make this country their vacation destination. Tourism in Indonesia plays an essential role in the domestic economy, contributing to Gross Domestic Product. With its abundant natural and cultural resources, North Sumatra has long been recognized as an attractive destination for foreign tourists. However, the tourism sector faces significant challenges related to fluctuations in the number of visits, mainly due to the impact of the COVID-19 pandemic, which has disrupted global travel patterns and caused considerable uncertainty in tourism forecasting. Therefore, predicting the number of tourist visits becomes crucial for effectively planning and managing tourist destinations. This research aims to compare the performance of two forecasting algorithms, SVM and linear regression, in predicting foreign tourist visits in North Sumatra using historical data from 2019 to 2023. The dataset was subjected to a preprocessing phase to ensure data cleanliness and consistency, focusing on key variables such as seasonal trends, external factors, and market dynamics. Both models were evaluated based on two commonly used accuracy metrics, MAPE and RMSE, to assess how well the models could predict actual tourist arrivals. The results of the study indicate that Linear Regression outperforms SVM in terms of prediction accuracy, with a MAPE of 42.40% and an RMSE of 6735.6, compared to SVM with a MAPE of 46.65% and an RMSE of 8020.42. These findings provide valuable insights for local government authorities and tourism industry stakeholders to enhance destination planning, resource allocation, and strategies to attract more foreign tourists in the post-pandemic era.
Comparison of K-Medoids and K-Means Result for Regional Clustering of Capture Fisheries in Aceh Province Thifal Salsabila; Nurdin Nurdin; Sujacka Retno
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.829

Abstract

This research aims to develop a web-based application that can categorize areas of capture fisheries in Aceh Province. The methods used in this research are K-Means and K-Medoids. The methods used in this research are K-Means and K-Medoids, a clustering technique used to group districts/cities based on high and low catch areas. This application will use data from the Marine and Fisheries Service (KKP) of Aceh Province, covering the period 2017 to 2023. This research will analyze variables such as production (tons), number of vessels, sub-districts, villages, and fish species. The system is developed using the PHP programming language to facilitate implementation and data access by stakeholders. Stakeholders. As an evaluation tool for clustering results, the Davies-Bouldin Index (DBI) is used to measure the quality of clustering results. The results of this study are expected to provide an overview of areas with high catches and assist policymakers in designing a more strategic approach to fishing—policymakers in developing more effective strategies to increase fishing, especially in districts with low fish catch. In addition, this application also provides an interactive platform for users to analyze fisheries data quickly and efficiently.
Sentiment Analysis of Google Maps User Reviews on the Play Store Using Support Vector Machine and Latent Dirichlet Allocation Topic Modeling Violita Aditya Zahrah; Nurdin Nurdin; Risawandi Risawandi
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.580

Abstract

These days, traveling is made easier by utilizing easily accessible online directions such as Google Maps. Google Maps provides real-time routes by displaying and presenting the closest routes that users can take. However, lately, the routes provided by Google Maps services often get users lost by presenting routes such as forests, narrow roads, and even dead ends. Therefore, this study aims to determine the level of user satisfaction and sentiment into two categories, namely positive and negative, based on reviews on the Google Play Store platform using the Support Vector Machine (SVM) algorithm and topic modeling using Latent Dirichlet Allocation (LDA) to find out the collection of topics that are the main topics of conversation by users regarding Google Maps services. The results of this study show that the SVM algorithm is feasible to use in sentiment analysis classification with an accuracy value of 86%, precision of 93%, recall of 53%, and f1-score of 52%. In addition, topic modeling is applied to generate coherence values for each topic, which shows that the higher the coherence value, the more specific the topic is. The highest coherence value generated in this study was two topic models with a coherence value of 35.15%, but this study took five with a coherence value of 33.39%. The five topic models to be applied in this study are selected because they have a good enough coherence value to identify the main topics and hidden topics in Google Maps user reviews with the Latent Dirichlet Allocation model. The topic model shows five aspects users often discuss: Google Maps route accuracy, system and service errors, navigation application directions, lost time history, and convoluted route provision.
Students' Perceptions of Learning Using Powtoon Based on Gender in SMP/MTS Fajriana Fajriana; Safriana Safriana; Nurdin Nurdin
International Journal of Engineering, Science and Information Technology Vol 2, No 1 (2022)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (426.573 KB) | DOI: 10.52088/ijesty.v2i1.205

Abstract

Perception is an individual observation or process giving meaning as a result of observations about an object, event, and so on through his five senses, which is obtained by inferring information and interpretation of messages so that someone can provide feedback about the good or bad or the positive and negative of it. The use of animation media in learning other than providing many conveniences, there are also obstacles, causing various student perceptions. Students' perceptions of the use of learning media can be seen by gender. This study aims to determine students' perceptions of learning using animation/powton in SMP/MTs based on gender. Based on the data collected, this research is descriptive with a qualitative approach. The data collection method in this study used a questionnaire in collecting data from primary and secondary sources. In this study, the primary data was the result of a perception survey through a questionnaire conducted to SMP/MTs students. In contrast, secondary information is obtained from a literature review regarding online learning systems and virtual classes. The data analysis technique uses percentages. The results of data analysis are presented descriptively in the following subchapter. Based on the results of research and discussion, it has been known that students' perceptions of learning mathematics using Powton media are perfect. Both male and female students positively perceive learning mathematics by using Powton-based learning media in SMP/MTs. Students get an average student answer of 100% and fall into the "Very Good" category. it explains that animation media is beneficial for students in understanding the lesson.
Co-Authors - Miranda ., Muthmainah Adi Prasetyo Adzuha Desmi Afif Diapari Ma'aruf Lubis Afif Diapari Aflizar Aflizar Afrilia, Yesy Ahmad Junaidi Aidilof, Hafizh Al Kautsar Aji Anggara Al Khaidar Alaiya, Azna Aldi Wahdana Alqhifari, Azka Ama Zanati Amalia, Nova Amin Munthoha Ananda Faridhatul Ulva Andri Alfitra Andriyan Ginting Annisa Karima Ansharulhaq Aminsyah Arnawan Hasibuan Asrianda Asrianda Aynun, Aynun Aynun, Nur Azzanna, Maghriza bhakti wan khaledy Bustami Bustami Bustami Bustami Cesilia, Yolinda Chaeroen Niesa Chicha Rizka Gunawan Cindy Cika Pradita Cut Agusniar Cut Agusniar Cut Rifa Salsabil Dadang Priyanto Dahlan Abdullah Dahlan Abdullah Darmansyah, Arif Desky, Muhammad Aulia Dewi Astika Erni Susanti Eva Darnila Fachril Akbar Fadlisyah Fadlisyah Fadlisyah Fahrozi, Fazar Fajriana Fajriana Fajriana Fajriana Fajriana Fajriana, Fajriana Fasdarsyah Fasdarsyah fatimah Fatimah Fikhri, Aditya Aziz Fikran, Rifzan Fikri Fikri Gavinda, Virza Gilang Sidiq gunawan, chicha rizka Gunawan, Chichi Rizka Hafizh Al Kautsar Aidilof Hafizh Al Kautsar Aidilof Hafizh Al-Kautsar Aidilof Hamdhana, Defry Haniful Fikri Hari Sampurno Hasbul Hadi Herman Fithra Hermansyah Hermansyah I Made Ari Nrartha Ilham Manzis Ilyana, Anis Imanda, Nanda Intan Nuriani Ira Wati Irwansyahputra Irwansyahputra Isa, Muzamir Ismun Naufal Iza Rifna Jessika Jessika Jessika, Jessika Jikti Khairina Julia Ulfah Khaidar, Al Khairina, Jikti Khairul Fuadi Khairul Khairul, Khairul Khairuni Khairuni Khalis Al Muqarrabin Khananda Raihansyah Kurnia, Sri Kurniawati Kurniawati Kurniawati Lisa Mulia Al Ikhlas M Farhan Aulia Barus M Raisal Al Farisi M Rizwan M Sayuti M Suhendri M. Ali, Rahmadi Maksal Mina Marleni Marleni Maryana Maryana Maryana Maryana Maryana Maulita, Maya Maya Juwita Dewi Maysura Meisya Syahtira Meriatna Meriatna Muchlis Abdul Muthalib Muchlish Abdul Muthalib Muhammad Daud Muhammad Faisal Muhammad fauzan Muhammad Fikry Muhammad Furqan, Muhammad Muhammad Hutomi Muhammad Iqbal Muhammad Johan Setiawan Muhammad Nasir Muhammad Nasrullahil Wafi Muhammad Reza Zainal Muhammad Riansyah Muhammad Ridha Muhammad Sayuti Muhammad Sayuti Muhammad Sayuti Mukhtar Anas Mukti Qamal Muliana, Syarifah Munirul Ula Munirul Ula Munirul Ula Mutammimul Ula Muzakir Nur Nadilla Baimal Puteri Nanda Imanda NELI SUSANTI, NELI Nunsina, Nunsina Nurhabsah Nurhabsah OK Muhammad Majid Maulana Rahma Jihan Ananta Rahmad Rahmad Rahmad Rahmat Rahmat Raihan Putri Rasyada, Reza Dian Reza, Restu Rini Meiyanti Risawandi, Risawandi Riza Mirza Rizal S.Si., M.IT, Rizal Rizki Setiawan Rizki Suwanda Rizky Putra Fhonna Rizkya, Ghinni Robi Kurniawan Rusadi, Athirah Safriana Safriana Said Fadlan Anshari Salahuddin Salahuddin Salamah Salamah Salimuddin, Salimuddin Samudera, Brucel Duta Sapitri, Anggri Sari, Cut Jora Sayuti, Muhammad Siagian, Tania Annisa Siregar, Widyana Verawaty Siti Hajar Sri Kurnia Sri Kurnia Suci Fitriani, Suci Suhaili Sahibul Muna Sujacka Retno Sultan, Kana Suryana, Fitra Syandriani Harahap Taufik Taufik Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Thifal Salsabila Uci Mutiara Putri Nasution Ulfah, Julia Ulva Fitriani Utomo, Muhammad Fikri Violita Aditya Zahrah Wahdana, Aldi Wan Dinulaqli Wan, Syahputra Wawan Syahputra Wawan Wawan Yani, Muhamamd Yeni Yeni Yesy Afrilia Yesy Afrillia Yolinda Cesilia Yulisda, Desvina Zahratul Fitri Zahratul Fitri, Zahratul Zalfie Ardian Zara Yunizar Zara Yunizar Zharif Athaya Andarfi Zuraida Zuraida