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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Register: Jurnal Ilmiah Teknologi Sistem Informasi Syntax Literate: Jurnal Ilmiah Indonesia Communication in Biomathematical Sciences MITRA: Jurnal Pemberdayaan Masyarakat Jurnal ULTIMATICS Zero : Jurnal Sains, Matematika, dan Terapan Range : Jurnal Pendidikan Matematika Innovation in Research of Informatics (INNOVATICS) SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Engineering, Mathematics and Computer Science Journal (EMACS) Juminten : Jurnal Manajemen Industri dan Teknologi Indonesian Community Journal Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi dan Manajemen Industri Terapan eProceedings of Engineering Jurnal Pengabdian West Science Indonesian Journal of Statistics and Its Applications
Alifah, Amalia Nur
Telkom University
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PENGEMBANGAN WEBSITE SURABAYA DESIGN CENTER (STUDI KASUS TAMAN WISATA KAYOON SURABAYA) Alifah, Amalia Nur; Rizky Fenaldo Maulana; Dominggo Bayu Baskara; Fajrul Falah Arrafi; Qothrunnadaa Nahdah Dzakiyyah; Ananda Taqhsya Dwiyana
Aptekmas Jurnal Pengabdian pada Masyarakat Vol 6 No 4 (2023): APTEKMAS Volume 6 Nomor 4 2023
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36257/apts.v6i4.8319

Abstract

In the ever-evolving digital era, the importance of digital marketing as a key tool in promoting businesses and services is increasingly apparent. The Kayoon area, which was originally the center of the decorative stone and jewelry trade in Surabaya, has experienced a decline in visits along with the impact of the COVID-19 pandemic. As a solution, the Surabaya City Government in collaboration with ADIDES and PK-KPBI ITS, along with ITTelkom Surabaya, initiated a revitalization by turning Kayoon into the Surabaya Design Centre (SDC). SDC aims to be an Urban Education area and a center for branding original handicraft products, but faces challenges in visibility and interaction in the digital era. To overcome this, a community service program is designed to assist SDC in improving the promotion of its business, products, and services digitally through website creation, social media, and Google Business registration. With the implementation of this digital marketing strategy, it is hoped that SDC can strengthen its reputation as a center of innovation and creativity, expand audience reach, and create new business opportunities within the design community. Thus, this community service program activity is expected to help the Kayoon community to get more benefits and new opportunities in this digital era.
Pengaruh Motivasi dan Kepuasan Kerja Terhadap Prestasi Kerja dengan Metode Regresi Study Case : PT.X Nicko Nur Rakhmaddian; Amalia Nur Alifah
JUMINTEN Vol. 3 No. 1 (2022): Juminten: Jurnal Manajemen Industri dan Teknologi
Publisher : Teknik Industri - UPN "Veteran" Jatim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/juminten.v3i1.390

Abstract

Pandemi covid-19 pada saat ini telah banyak mengakibatkan banyak sektor ekonomi mengalami penurunan penjualan. Sektor yang terdampak negatif dari Covid-19 salah satunya merupakan UMKM yang bergerak pada bidang kuliner. UMKM X adalahUMKM yang bergerak pada bidang kuliner dengan menjual produk utama berupa bubur ayam. UMKM X terdampak covid-19 sehingga mengalami penurunan penjualan dari tahun ketahun. Tujuan dari penelitian ini ialah mengetahui variable-variable Marketing Mix 4p (Price, Promotion, Product, dan Place) apa saja yang dapat mempengaruhi penjualan produk sehingga bisa menjadi dasar pengambilan keputusan untuk meningkatkan penjualan UMKM X. Metode yang digunakan terdiri dari analisis regresi linier berganda, uji simultan dan uji parsial. Output atau Hasil dari penelitian didapatkan semua variable dari Marketing Mix mempengaruhi tingkat penjualan UMKM X terutama variable promosi dan variable place. Saran untuk UMKM X adalah lebih berfokus mengembangkan strategi promosi dan memperbaiki suasana tempat makan di UMKM X, seperti melakukan digitalisasi pemesanan produk dan membuat suasana tempat makan lebih nyaman, bersih dan tertata rapi.
Klasifikasi Tingkat Kedalaman Kemiskinan di Indonesia Menggunakan Support Vector Machine dan Regresi Logistik Mufaidah, Astikhatul; Ni'mah, Rifdatun; Nur Alifah, Amalia
eProceedings of Engineering Vol. 12 No. 5 (2025): Oktober 2025
Publisher : eProceedings of Engineering

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

Abstract

Abstrak — Kemiskinan merupakan masalah kompleks yang masih menjadi tantangan utama di Indonesia, dengan dampak yang luas terhadap kesejahteraan masyarakat. Penelitian ini bertujuan untuk mengklasifikasikan tingkat kedalaman kemiskinan menggunakan dua model machine learning yakni Support Vector Machine (SVM) dan Regresi Logistik, serta mengidentifikasi faktor-faktor yang secara signifikan memengaruhinya. Dataset yang digunakan mencakup variabel sosial-ekonomi dari berbagai wilayah, seperti Bantuan Sosial, Rata-Rata Lama Sekolah, dan Jumlah Penduduk. Hasil analisis menunjukkan bahwa model SVM dan Regresi Logistik sama sama menghasilkan performa klasifikasi yang tinggi, dengan akurasi 99%. Regresi Logistik pada penelitian ini digunakan untuk mengetahui faktor-faktor yang berpengaruh secara signifikan terhadap tingkat kedalaman kemiskinan melalui pen- dekatan uji signifikansi statistik. Regresi Logistik menunjukkan bahwa tiga variabel yang paling signifikan adalah Bantuan Sosial, Pendapatan Asli Daerah, dan Rata-rata Lama Sekolah, Temuan ini diharapkan dapat menjadi dasar bagi pengembangan kebijakan yang lebih tepat sasaran dalam upaya pengentasan kemiskinan di Indonesia Kata kunci— Analisis Data, Indeks Kedalaman Kemiskinan, Kemiskinan, Regresi Logistik, Support Vector Machine
Developing and Managing MSME Websites to Improve Kampoeng Ilmu's Operational Performance Alifah, Amalia Nur; Rachmaniar, Desita Nur; Mustaqim, Tanzilal; Rafif, Sulthan
SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Vol. 6 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/spekta.v6i2.13871

Abstract

Background: MSMEs in Kampoeng Ilmu Surabaya continue to face obstacles in product marketing and digital adoption, which limit their operational growth. This program aims to address these issues by developing an integrated website and improving digital literacy among MSME partners to strengthen their online visibility and business sustainability. Contribution: The program contributes to the community by providing a digital platform (UMKMCerdas website) that enables MSMEs to independently manage product data, storefront profiles, and promotional information. It also enhances partners’ capacity to operate digital tools, supporting long-term empowerment and competitiveness. Method: The implementation consisted of five structured stages: needs analysis through interviews, website design with UI/UX and database planning, website development using PHP (Laravel), MySQL, Tailwind, and Filament, training and mentoring for MSME partners, and evaluation through continuous monitoring. A participatory approach was used to ensure active involvement and skill transfer to 82 MSME actors. Results: The integrated website successfully provides features such as bookstore profiles, product catalogs, Google Maps integration, WhatsApp contacts, and an admin dashboard. Post-training responses showed significant enhancement in partners’ confidence and ability to use digital tools for promoting their businesses and managing information. Conclusion: The program effectively strengthens the digital capabilities of Kampoeng Ilmu MSMEs, enabling them to manage business content independently and expand their market reach.
Enhanced Rice Yield Prediction in Indonesia with Integrated Climate and Agricultural Data Using Decision Tree Regression Tegar Arifin Prasetyo; Samuel Jefri Siahaan; Usman Efendi; Mesya Angeliqa Hutagalung; Amalia Nur Alifah
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.5352

Abstract

Rice is central to Indonesia’s food security, yet provincial yields are highly sensitive to climatic variability, making reliable forecasting essential for national planning and improving farmer welfare. Most prior Indonesian yield models rely on rainfall and temperature data and omit sunlight exposure duration, which is a limiting factor for photosynthesis in the humid tropics where solar radiation, not temperature, often constrains productivity. This study develops a province-level rice yeild prediction model based on Decision Tree Regression (DTR) that integrates climate data from the Meteorology, Climatology, and Geophysics Agency (BMKG) with agricultural statistics from the Central Statistics Agency (BPS). The dataset comprises data from 34 provinces covering the period from 2018 to 2023 (204 province-year observations), with year, harvested land area, rainfall, and sunlight exposure duration as predictors and rice production as the target variable. The dataset was partitioned into training and testing subsets using an 80:20 ratio. Hyperparameter tuning was performed using k-fold cross-validation, and model performance was performed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The model attained an average RMSE of 93,046 tons (MAE ≈ 51,824 tons; MAPE ≈ 6.8% on the 2023 hold-out year). A key finding is that absolute RMSE is strongly scale-dependent. When evaluated in relative terms, the highest-producing Java provinces were among the most accurately predicted (relative RMSE ≈ 2.3–2.5%), whereas several small or structurally volatile provinces showed relative errors above 40%. The study contributes to the literature by providing (i) the explicit integration of sunlight exposure into Indonesian rice yield modeling, (ii) a province-disaggregated error analysis that reframes accuracy in scale-independent terms, and (iii) an interpretable decision-support tool for food-policy stakeholders such as Bulog and the Ministry of Agriculture.
Optimizing K-Means Clustering through Distance Metric Simulation for Strategic Enrollment Segmentation in Private Universities Regita Putri Permata; Amalia Nur Alifah; I Made Wisnu Adi Sanjaya
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33089

Abstract

K-Means clustering is a widely used unsupervised learning technique for identifying patterns and grouping data based on feature similarities. However, the effectiveness of K-Means significantly depends on the choice of distance metric. This study conducts a comprehensive simulation to evaluate and compare the performance of four distance metrics—Euclidean, Cityblock (Manhattan), Canberra, and Mahalanobis—in the context of strategic market segmentation for private universities. The dataset includes simulated and institutional data incorporating variables such as account creation, registration, graduation, student performance (social, science, and scholastic scores), income, and geographic distance. The results indicate that Euclidean and Cityblock distances yield efficient and interpretable clusters with low computational costs, whereas Mahalanobis distance, despite its capacity to model covariance, introduces computational overhead without proportional improvement in segmentation quality. Interestingly, Canberra distance produces compact clusters but offers no significant gain in separability. From the resulting segmentation, two clusters emerge as high-potential targets for marketing strategies: Cluster 0 (high-income and distant students) and Cluster 1 (diverse academic and socioeconomic profiles). The findings highlight the importance of aligning distance metric selection with specific clustering objectives and offer practical insights for data-driven strategic enrollment planning in private higher education institutions.
Rainfall Forecasting using Spatio-Temporal and Neural Network Study Case: Meteorological Data of Madura Island Ryanta Meylinda Savira; Regita Putri Permata; Amalia Nur Alifah; Yohanes Setiawan; Adzanil Rachmadhi Putra
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35091

Abstract

Rainfall forecasting is crucial in meteorological studies due to its significant impact on sectors such as agriculture, which is the main livelihood on Madura Island. This study aims to forecast rainfall on Madura Island using a hybrid approach that combines the Generalized Space-Time Autoregressive-X (GSTARX) model and Neural Network (NN). The data used consist of daily rainfall records from Bangkalan, Sampang, Pamekasan, and Sumenep, covering the period from January 2013 to December 2023. Data from January 2013 to September 2023 were used for training, while data from October to December 2023 were used for testing. The GSTARX model was employed to capture spatio-temporal patterns, while the NN was applied to learn the non-linear relationships in the residuals. The results show that the GSTARX model effectively captures rainfall patterns, though some differences remain compared to the actual data, with RMSE values of Bangkalan (1.514), Sampang (0.256), Pamekasan (0.477), and Sumenep (0.127). Meanwhile, the hybrid GSTARX-FFNN model achieved improved forecasting performance in Sampang (0.392), Pamekasan (0.679), and Sumenep (0.412), although Bangkalan recorded a higher RMSE (1.359). Overall, the GSTARX model proved more effective in forecasting rainfall on Madura Island, delivering smaller and more consistent prediction errors.
Peningkatan Kemampuan Guru dalam Visualisasi Data Interaktifsebagai Strategi Penguatan Literasi Data melaluiPemanfaatan Looker Studio Rifdatun Ni'mah; Amalia Nur Alifah; Ferdyan Paskah Dyka; Helisyah Nur Fadhilah
MITRA: Jurnal Pemberdayaan Masyarakat Vol. 10 No. 1 (2026): Mitra: Jurnal Pemberdayaan Masyarakat
Publisher : Institute for Research and Community Services

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/mitra.v10i1.7046

Abstract

Rendahnya literasi data di lingkungan sekolah, khususnya pada jenjang Sekolah Menengah Pertama (SMP), menjadi tantangan dalam mewujudkan pembelajaran berbasis data. Observasi awal menunjukkan bahwa guru di SMP Hang Tuah 4 Surabaya belum terbiasa memanfaatkan data akademik secara visual maupun interaktif. Kegiatan pengabdian ini bertujuan untuk meningkatkan kompetensi guru dalam mengolah dan memvisualisasikan data menggunakan Looker Studio sebagai langkah awal memperkuat literasi data siswa. Metode pelaksanaan berupa pelatihan, pendampingan, dan praktik langsung menggunakan data nilai siswa. Materi pelatihan meliputi pengantar literasi data, pengolahan data sederhana, hingga pembuatan dashboard interaktif. Hasil pelatihan menunjukkan peningkatan pemahaman yang signifikan, dengan rerata skor pre-test sebesar 53,75 meningkat menjadi 78,13 pada post-test, dan didukung oleh hasil uji t berpasangan yang signifikan secara statistik (p < 0,05). Setiap peserta berhasil menyusun dashboard nilai yang menampilkan grafik interaktif dengan filter kelas dan mata pelajaran. Modul pelatihan yang disusun secara bertahap juga berkontribusi besar dalam mendukung pemahaman peserta. Kegiatan ini tidak hanya meningkatkan keterampilan teknis guru, tetapi juga membangun kesadaran akan pentingnya komunikasi data yang informatif dan kontekstual dalam proses pembelajaran.
Ordinal Logistic Regression Model of Micro, Small, and Medium-Sized Enterprises Income: A Case Study of Micro, Small and Medium-Sized Enterprises in Surabaya Amalia Nur Alifah; Almira Ivah Edina; Mawanda Almuhayar
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p143-154

Abstract

Micro, Small, and Medium Enterprises (MSMEs) is a business sector that is able to make a significant contribution to economic recovery in Indonesia. In Surabaya, there are many MSMEs with various fields, both food and non-food sectors which include services, trade, etc. MSMEs actually have great potential to boost the economic growth of the people of Surabaya. Especially during the COVID-19 pandemic, MSMEs owners must be able to strategize how their income can be stable or even bigger. Therefore, it is very important to know what factors can boost MSMEs income in Surabaya. In this study, it will be examined what factors can affect the income of MSMEs in Surabaya. The method used in this study is Ordinal Logistic Regression which aims to determine which independent variables or factors affect the dependent variable which in this case is MSMEs income. Based on the results of the analysis, it can be seen that the variables that affect MSMEs income are MSMEs Location, MSME Activities, and MSME Outreach. Keywords: ordinal logistic regression, MSMEs, income.
Optimalisasi Pengelolaan Website Sekolah dan Penguatan Literasi Data Melalui Pelatihan Pembuatan Konten Amalia Nur Alifah; Ahmad Wali Satria Bahari Johan; Nuchaila Ainiyah; Hafshah Hafshah; Faza Nur Aulia Suraya; Feysha Kamila Pracilya; Clairine Anargya Athallah; Talitha Shafa Azzahra
Jurnal Pengabdian West Science Vol 5 No 07 (2026): Jurnal Pengabdian West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jpws.v5i07.3650

Abstract

Optimasi situs web sekolah sangat penting untuk menyebarluaskan informasi dan meningkatkan citra lembaga pendidikan di era digital. Namun, banyak sekolah yang tidak mampu mengelola situs webnya secara aktif karena keterbatasan kompetensi guru dalam pembuatan konten digital dan literasi data. Program pengabdian masyarakat ini bertujuan untuk meningkatkan kemampuan guru dalam mengelola konten situs web serta memperkuat literasi data melalui pendekatan pelatihan partisipatif dan kontekstual. Kegiatan ini dilaksanakan pada tanggal 4 Juni 2026, melibatkan 13 guru melalui ceramah, demonstrasi, studi kasus, dan praktik langsung. Evaluasi menggunakan tes awal dan tes akhir menunjukkan peningkatan nilai rata-rata dari 55,54 menjadi 74,92, yang dikonfirmasi oleh uji-t berpasangan (t = 3,689; p = 0,003). Seluruh peserta berhasil menghasilkan konten situs web berkualitas tinggi dalam hal struktur, keterbacaan, kelengkapan data, dan kategorisasi. Program ini meningkatkan kompetensi teknis sekaligus menumbuhkan kesadaran literasi data serta praktik pengelolaan informasi yang akuntabel di lingkungan sekolah.
Co-Authors Adzanil Rachmadhi Putra Ahmad Wali Satria Bahari Johan Almira Ivah Edina Almira Ivah Edina Alviano, Muhammad Fadhil Ananda Taqhsya Dwiyana Andini, Galuh Ardhani, Calista Ghea Clairine Anargya Athallah Dani Suandi Dermawan, Dwi Bagus Desita Nur Rachmaniar Desita Nur Rachmaniar Dominggo Bayu Baskara Fajrul Falah Arrafi Fannisa Egista Naya Faza Nur Aulia Suraya Ferdyan Paskah Dyka Feysha Kamila Pracilya Hafshah Hafshah Helisyah Nur Fadhilah Helisyah Nur Fadhilah Helisyah Nur Fadhilah I Made Wisnu Adi Sanjaya Imroatul Khoiriyah Muwahidah Ira Prapti Ningrum Izzah, Nurul Larasati, Harashta Tatimma Mawanda Almuhayar Mazi Prima Reza Mesak, Evellyn Verity Mesya Angeliqa Hutagalung Mufaidah, Astikhatul Muwahidah, Imroatul Khoiriyah Nicko Nur Rakhmaddian, Nicko Nur Ningrum, Ira Prapti Ningtyas , Miranthy Pramita Nuchaila Ainiyah Nurul Izzah Purwaningrum, Alfi Qothrunnadaa Nahdah Dzakiyyah Raditya, Helga Fadhil Regita Putri Permata Reza, Mazi Prima Rifdatun Ni'mah Rifdatun Ni’mah Rizky Fenaldo Maulana Ryanta Meylinda Savira Samuel Jefri Siahaan Sulthan Rafif Talitha Shafa Azzahra Tanzilal Mustaqim Tegar Arifin Prasetyo Usman Efendi Vivi Indah Rahmawati Wachda Yuniar Rochmah Yohanes Setiawan