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Identifikasi Pola Konflik Lahan Perkebunan di Lingkungan PTPN Group Berbasis Data Hukum Menggunakan Hierarchical Clustering dengan Algoritma Agglomerative Wismarini, Theresia; Eniyati, Sri; Lestariningsih, Endang; Soelistijadi, Soelistijadi; Ardhianto, Eka
JURNAL FASILKOM Vol. 14 No. 3 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i3.7915

Konflik lahan perkebunan sangat penting untuk dideteksi secara dini, karena potensi dampaknya terhadap berbagai aspek seperti kesehatan, ekosistem, pertanian, dan jaringan listrik. Penelitian ini bertujuan mengidentifikasi pola konflik lahan perkebunan di lingkungan PTPN Group berbasis data hukum menggunakan teknik Hierarchical Clustering dengan algoritma Agglomerative Clustering. Deteksi dini konflik lahan penting karena dampaknya terhadap kesehatan, ekosistem, pertanian, dan infrastruktur. Studi ini mengolah data hukum dari Mahkamah Agung RI, data geospasial dari OpenStreetMap, dan data sosial-ekonomi dari BPS dan World Bank untuk menganalisis dan mengelompokkan pola konflik. Proses analisis meliputi inisialisasi data, penghitungan jarak, penggabungan klaster, dan visualisasi dendrogram. Hasilnya menunjukkan bahwa algoritma ini efektif dalam mengelompokkan konflik lahan berdasarkan karakteristik populasi dan GDP yang berbeda, membantu memahami hubungan antar kasus hukum. Penelitian ini berkontribusi dengan mengidentifikasi faktor utama pemicu konflik lahan untuk mendukung manajemen lahan berbasis data. Rekomendasi kebijakan publik mencakup penetapan zona prioritas penyelesaian konflik, optimalisasi pengawasan hukum berbasis data, dan peningkatan transparansi dalam tata kelola lahan guna mencegah eskalasi konflik di wilayah PTPN Group.

Implementasi Metode ANP Untuk Pemberian Bantuan Sosial Wulandari, Putri; Soelistijadi, R.; Lestariningsih, Endang
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.489

Poverty is a condition of a person or group of people with limited assets and valuables. With these limitations, the community is unable to finance the necessities of a decent life. Standards for the needs of a person’s life worth include food and drink, clothing, a place to live or a house,work and so on. The selection of social assistance recipients is still done manually so that it will affect the outcome of the decision. So, it is necessary to create a system that helps the decision support process for social assistance recipient the ANP (Analytic Network Process) method. In ANP there are 7 (seven) stages, including : determining alternatives, determining criteria, determining alternative comparisons for each node in the criteria cluster, determining the comparison of criteria for each node in the alternative cluster, calculating the weighted supermatrix, calculating the unweighted supermatrix and supermatrix limit. The alternative refers to the name of the community that is the candidate for assistance and the criteria refers to a requirement for social assistace recipients in the Bumirejo sub-district. The first step is calculating ANP by inputting alternative data and criteria data and determining an alternative comparison table for nodes in each criterion cluster and vice versa, in order to produce an index and consistency ratio. The implementation of ANP determines an unweighted and weighted supermatrix with a limit supematrix so as to produce a rangking of decision support system for determining poverty ranking in the provision of social assistance with the lowest synthesis value of 0,11824021. so it can be concluded that this final result can be used as a benchmark for social assistance recipients based on predetermined criteria

Penerapan Metode SAW Pada Rekomendasi Pemilihan Jenis Jamur Untuk Budidaya Dan Konsumsi Nurrohim, Muhammad; Lestariningsih, Endang; Nurraharjo, Eddy
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.508

Advances and developments in technology are currently getting faster, because technology has many uses. One of them is technology is used to assist in making decisions. The aim of this research project is to design and build a decision support system to determine which mushroom plants can be cultivated and consumed safely, resulting in a recommendation system for selecting mushroom species for cultivation and consumption for users or the community. In recent years, mushrooms have become a hot topic of conversation in Indonesia. Currently, people in Indonesia are starting to like to consume various processed foods made from consumption mushrooms. So that the raw material for production, namely mushrooms, is needed quite a lot. In the end, several elements of society began to peek at business opportunities to cultivate consumption mushrooms to earn income. The method that will be applied in the system is the SAW (Simple Additive Weighting) method. The workings of the method is to perform calculations, namely calculating the weighted number of rating criteria on all attributes in each alternative then producing the largest value which is selected as the best alternative and a thorough ranking is carried out. The results obtained are for oyster mushrooms to be ranked as the best alternative with the largest value of 23

Pendekatan Graph-Based Community Detection dalam Social Network Analysis Jaringan Undang-Undang Republik Indonesia 2014-2024 Wibisono, Setyawan; Wahyudi, Eko Nur; Hadikurniawati, Wiwien; Lestariningsih, Endang; Cahyono, Taufik Dwi
Dinamik Vol 30 No 2 (2025)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v30i2.10218

This study evaluates the performance of three community detection algorithms—Leiden, Infomap, and Label Propagation—on the legal network of the Republic of Indonesia spanning the period 2014–2024. The network consists of 679 nodes and 2,295 edges, constructed based on citation relationships among regulations. The evaluation employs four network topology metrics: modularity, coverage, conductance, and inter-cluster density. Results show that the Leiden algorithm achieves the highest modularity score (0.522991), indicating the formation of communities with strong internal density. Additionally, it yields the lowest conductance value (0.302455), suggesting relatively well-isolated communities. In contrast, the Label Propagation algorithm produces the highest coverage (0.835294) and inter-cluster density (0.542331), but with a lower modularity (0.431583), reflecting the formation of large communities with less distinct boundaries. Infomap exhibits moderate performance, with a modularity score of 0.508406 and inter-cluster density of 0.420803, yet records a relatively high conductance (0.410409). Network visualizations reveal three major communities for each algorithm, representing thematic clusters such as institutional governance, constitutional law, and public finance. Overall, the Leiden algorithm is considered the most optimal for detecting modular, stable, and thematically coherent community structures within the complex and interrelated network of Indonesian laws.

Social Network Analysis untuk Pemeringkatan Popularitas Makanan Cepat Saji Menggunakan Metode PSI Wibisono, Setyawan; Hadikurniawati, Wiwien; Lestariningsih, Endang; Wahyudi, Eko Nur; Cahyono, Taufiq Dwi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 1 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

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This research aims to rank the popularity of fast-food brands in Indonesia based on Twitter conversations using the Preference Selection Index (PSI) method and validate the results with COPRAS and AHP-COPRAS methods. Data were obtained by crawling Twitter from April 21, 2023, to April 28, 2023. Seven well-known brands, such as KFC, MCD, PizzaHut, Hokben, Solaria, JCo, and Richeese, were evaluated as alternatives using eight criteria through Social Network Analysis. The criteria were categorized into advantageous and disadvantageous, and preference values were calculated using PSI. After normalizing the decision matrix, calculations were performed for preference variation and overall preference values. Alternatives were ranked based on the preference selection index, and the results were validated with COPRAS and AHP-COPRAS. The results revealed significant differences in rankings between the PSI method and others. The alternative that received the highest rank changed from A2 (COPRAS and AHP-COPRAS) to A3 (PSI). This emphasizes the importance of choosing the right method for brand ranking, as it can influence decision-making. Method validation through result comparison with other methods provides additional insights into the reliability of the PSI method in the context of this research.

Optimalisasi Model Klasifikasi Diabetes Menggunakan Ensemble Learning Adaboost, Gradient Boosting, dan XGBoost Wibisono, Setyawan; Hadikurniawati, Wiwien; Yulianton, Heribertus; Lestariningsih, Endang; Cahyono, Taufiq Dwi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 2 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

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Diabetes mellitus adalah penyakit kronis yang memengaruhi jutaan orang secara global dan membutuhkan metode diagnosis dini untuk mencegah komplikasi. Penelitian ini bertujuan untuk mengoptimalkan prediksi diabetes dengan membandingkan tiga metode ensemble learning: AdaBoost, Gradient Boosting, dan XGBoost. Dataset yang digunakan adalah Diabetes Health Indicators, yang menggabungkan indikator kesehatan seperti tekanan darah, kolesterol, dan kebiasaan gaya hidup. Tahapan penelitian meliputi pemrosesan data, pengembangan model, serta eval_uasi performa menggunakan metrik akurasi, presisi, recall, F1-score, dan AUC (Area Under the Curve). Hasil menunjukkan bahwa Gradient Boosting unggul dalam akurasi dan AUC, menandakan kemampuan yang lebih baik dalam mendeteksi diabetes secara konsisten dibandingkan dengan dua metode lainnya. AdaBoost memperlihatkan keseimbangan yang baik antara presisi dan recall, menjadikannya cocok untuk skenario yang memerlukan pengendalian kesalahan positif dan negatif secara proporsional. Sementara itu, XGBoost menawarkan efisiensi pemrosesan yang optimal dengan performa yang kompetitif. Gradient Boosting direkomendasikan untuk aplikasi klinis yang membutuhkan akurasi tinggi, sedangkan AdaBoost dapat menjadi alternatif ketika keseimbangan prediksi menjadi prioritas. Penelitian ini berkontribusi dalam pengembangan alat prediksi diabetes yang lebih akurat, efektif, dan dapat diterapkan di sektor kesehatan untuk mendukung upaya deteksi dini.

Social Network Analysis untuk Pemeringkatan Popularitas Makanan Cepat Saji Menggunakan Metode PSI Wibisono, Setyawan; Hadikurniawati, Wiwien; Lestariningsih, Endang; Wahyudi, Eko Nur; Cahyono, Taufiq Dwi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 1 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

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

This research aims to rank the popularity of fast-food brands in Indonesia based on Twitter conversations using the Preference Selection Index (PSI) method and validate the results with COPRAS and AHP-COPRAS methods. Data were obtained by crawling Twitter from April 21, 2023, to April 28, 2023. Seven well-known brands, such as KFC, MCD, PizzaHut, Hokben, Solaria, JCo, and Richeese, were evaluated as alternatives using eight criteria through Social Network Analysis. The criteria were categorized into advantageous and disadvantageous, and preference values were calculated using PSI. After normalizing the decision matrix, calculations were performed for preference variation and overall preference values. Alternatives were ranked based on the preference selection index, and the results were validated with COPRAS and AHP-COPRAS. The results revealed significant differences in rankings between the PSI method and others. The alternative that received the highest rank changed from A2 (COPRAS and AHP-COPRAS) to A3 (PSI). This emphasizes the importance of choosing the right method for brand ranking, as it can influence decision-making. Method validation through result comparison with other methods provides additional insights into the reliability of the PSI method in the context of this research.

Optimalisasi Model Klasifikasi Diabetes Menggunakan Ensemble Learning Adaboost, Gradient Boosting, dan XGBoost Wibisono, Setyawan; Hadikurniawati, Wiwien; Yulianton, Heribertus; Lestariningsih, Endang; Cahyono, Taufiq Dwi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 2 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

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

Diabetes mellitus adalah penyakit kronis yang memengaruhi jutaan orang secara global dan membutuhkan metode diagnosis dini untuk mencegah komplikasi. Penelitian ini bertujuan untuk mengoptimalkan prediksi diabetes dengan membandingkan tiga metode ensemble learning: AdaBoost, Gradient Boosting, dan XGBoost. Dataset yang digunakan adalah Diabetes Health Indicators, yang menggabungkan indikator kesehatan seperti tekanan darah, kolesterol, dan kebiasaan gaya hidup. Tahapan penelitian meliputi pemrosesan data, pengembangan model, serta eval_uasi performa menggunakan metrik akurasi, presisi, recall, F1-score, dan AUC (Area Under the Curve). Hasil menunjukkan bahwa Gradient Boosting unggul dalam akurasi dan AUC, menandakan kemampuan yang lebih baik dalam mendeteksi diabetes secara konsisten dibandingkan dengan dua metode lainnya. AdaBoost memperlihatkan keseimbangan yang baik antara presisi dan recall, menjadikannya cocok untuk skenario yang memerlukan pengendalian kesalahan positif dan negatif secara proporsional. Sementara itu, XGBoost menawarkan efisiensi pemrosesan yang optimal dengan performa yang kompetitif. Gradient Boosting direkomendasikan untuk aplikasi klinis yang membutuhkan akurasi tinggi, sedangkan AdaBoost dapat menjadi alternatif ketika keseimbangan prediksi menjadi prioritas. Penelitian ini berkontribusi dalam pengembangan alat prediksi diabetes yang lebih akurat, efektif, dan dapat diterapkan di sektor kesehatan untuk mendukung upaya deteksi dini.

Optimalisasi Pemilihan Lapangan Badminton: Integrasi Metode SAW dan AHP dalam Sistem Pendukung Keputusan Lestariningsih, Endang; Nadila, Latifia; Handoko, Widiyanto Tri; Amin , Imam Husni Al
MEANS (Media Informasi Analisa dan Sistem) Volume 10 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

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Badminton is one of the most popular sports in the world, with Indonesia having achieved great success in international competitions. The high level of public interest in this sport requires the availability of adequate court facilities. However, the comfort of badminton court facilities still varies, so a decision support system is needed to help select the optimal court. This study develops a decision support system based on the integration of the Simple Additive Weighting (SAW) and Analytical Hierarchy Process (AHP) methods. SAW is used for criterion weighting, while AHP is applied in the ranking stage of alternatives. This combination of methods was chosen due to its ability to process qualitative data from player and athlete evaluations, as well as quantitative court data, while addressing complex issues through hierarchy-based prioritisation. The results of the study indicate that the system is capable of consistently weighting criteria (CR < 0.1). This study also produced a ranking of badminton courts in North Brebes based on seven criteria (price, floor type, lighting, bathrooms, scoreboards, cooperatives, and parking lots) and identified priority facilities. Court 33 in Brebes was selected as the best alternative with a preference value of 0.7485.

MENINGKATKAN DAYA TARIK PEMBELAJARAN INFORMATIKA MELALUI DESAIN VISUAL DENGAN CANVA DI SMA NEGERI 1 BRINGIN KABUPATEN SEMARANG Wismarini, Theresia Dwiati; Murti, Hari; Lestariningsih, Endang; Redjeki, Rara Sriartati; Hartono, Budi; Anwar, Sariyun Naja; Lusiana, Veronica; Ardhianto, Eka
Intimas Vol 5 No 2 (2025)
Publisher : Fakultas Teknologi Informasi dan Industri Unisbank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/intimas.v5i2.10149

The rapid development of information technology requires teachers to present learning materials in a creative and visual manner. Informatics teachers at SMA Bringin face challenges in creating visually engaging teaching materials due to limited graphic design knowledge, which results in low student engagement and less effective delivery of content. To address this issue, a community service activity in the form of Canva training for informatics teachers at SMA Bringin was conducted. This activity followed a participatory and hands-on approach (learning by doing). The training took place over two days in the school's computer lab and was attended by 15 informatics teachers as well as several other subject teachers. Evaluation results showed 35% improvement in participants' understanding of Canva, based on a comparison of pre-test and post-test scores. Participants also displayed high creativity and enhanced visual skills in their final tasks, which involved creating educational materials using Canva. The satisfaction survey revealed that 93% of participants found the training highly beneficial and expressed their intention to adopt Canva in regular teaching. Overall, this training successfully improved the digital literacy of teachers, particularly in designing visual teaching media using Canva. Participants provided feedback suggest the organization of follow-up training focusing on animation or interactive video-based learning.

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