bluuebunny/crossref_metadata_2025 huggingface dataset:
Asas Kebebasan Berkontrak dalam Perjanjian Surogasi di Indonesia dari Perspektif Hukum Perdata dan Etika
Authors: Martiana Annisa Ayu
Published: December 2024

Setiap pasangan berharap memiliki anak untuk melanjutkan keturunan, namun beberapa mengalami kesulitan. Teknologi kedokteran modern seperti In Vitro Fertilization (IVF) menawarkan solusi melalui metode surogasi atau sewa rahim yang melibatkan kontrak antara pasangan dan wanita pengganti. Di Indonesia praktik ini belum memiliki dasar hukum yang jelas, sehingga menimbulkan dilema etis dan hukum. Penelitian ini memiliki tujuan dalam rangka mengeksplorasi penerapan asas kebebasan berkontrak pada perjanjian surogasi dari perspektif hukum perdata dan etika. Berdasarkan Pasal 1320 KUHPerdata, hasil penelitian menunjukkan bahwa perjanjian surogasi tidak memenuhi syarat objektif sahnya suatu perjanjian karena objek perjanjian berupa rahim tidak dapat dianggap sebagai benda atau barang dalam hukum perdata dan komersialisasi rahim bertentangan dengan nilai moral serta kesusilaan yang berlaku, sehingga batal demi hukum.
Pemanfaatan Agen Biologis dalam Pengelolaan Sampah Organik Rumah Tangga di Dusun Bejen Kabupaten Bantul
Authors: Nuryady Moh. Mirza , Fauzi Ahmad , Setyawan Dwi , Nurwidodo Nurwidodo , Pratiwi Ambar , Utami Inggita , Putra Ichsan Luqmana Indra , Munir Misbahul
Published: December 2024

Sampah organik merupakan hasil dari sisa makan, kayu, ranting, daun, dan kertas, sampah organik mendominasi di Indonesia. Salah satu penghasil sampah organik adalah rumah tangga. Dusun Bejen kabupaten Bantul merupakan penghasil sampah organik tersebut. Dengan adanya Pengabdian Kepada Masyarakat (PKM) menambah pengetahuan dan keterampilan serta kesadaran Warga Dusun Bejen Bantul meningkat dan permasalahan sampah yang belum tertangani di lokasi tersebut dapat terselesaikan. Kegiatan ini dilaksanakan di halaman Musholla Al-Manar, Dusun Bejen pada tanggal 19 Oktober 2021. Pelatihan yang dilakukan berupa pengolahan sampah organik dengan teknik keranjang takakura, eco-enzyme dan ember tumpuk dengan Black Soldier Fly (BSF). Dari hasil pengabdian kepada masyarakat Dusun Bejen terbukti meningkatkan pengetahuan dan keterampilan dalam mengolah sampah organik skala rumah tangga.
Edukasi Nilai Pancasila Melalui Game Si Panca di Sekolah SD Muhammadiyah 18 Surabaya
Authors: Hakim Lukman , Sya’ban Ma’ruf , Afifah Fauziana Windyanisa
Published: December 2024

Pancasila merupakan dasar negara Indonesia yang memiliki nilai – nilai luhur. Pancasila diharapkan mampu menjadi acuan warga negara Indonesia dalam berperilaku dalam kehidupan sehari hari. Mengajarkan nilai – nilai Pancasila kepada siswa sekolah dasar merupakan langkah yang baik dalam membangun generasi unggul sejak dini. Penggunaan media digital sebagai alat pembelajaran mampu membantu guru dan siswa dalam memahami nilai – nilai Pancasila dengan cara yang mudah dan menyenangkan. Selain itu, kegiatan pengabdian masyakarat ini mampu membantu sekolah dalam mengembangkan metode pembelajaran baru menggunakan media game virtual. Game Si Panca merupakan luaran yang memberikan manfaat bagi guru dan siswa dalam mempelajari nilai – nilai Pancasila. Game Si Panca sebagai media pembelajaran digital cukup efektif dan memberikan dampak yang baik bagi siswa dalam memahami nilai – nilai Pancasila di kehidupan sehari-hari.
Transformasi Perilaku Menuju Zero Waste Melalui Edukasi Penggunaan Tumbler
Authors: Hastomo Widi , Saputro Ahmad Eko , Hudaa Syihaabul , Ambardi Ambardi , Chufran , Indra Bakti , Fitriansyah Reza
Published: December 2024

Kampanye tumbler telah berhasil meningkatkan kesadaran siswa dan komunitas sekolah tentang dampak negatif limbah plastik terhadap lingkungan. Melalui kampanye ini, pola konsumsi siswa dan seluruh entitas sekolah mengalami perubahan positif, dengan lebih banyak orang beralih dari penggunaan plastik sekali pakai ke penggunaan tumbler yang ramah lingkungan. Komitmen siswa terhadap penggunaan tumbler mencapai 93%, yang membuktikan pentingnya pendidikan dalam membentuk perilaku berkelanjutan dan gaya hidup ramah lingkungan. Kampanye ini berhasil meningkatkan pemahaman tentang pentingnya tumbler sebagai alternatif pengganti plastik sekali pakai, serta diharapkan dapat mendorong sekolah untuk menerapkan kebijakan pengurangan limbah plastik di lingkungan sekolah. Kegiatan pengabdian masyarakat ini merupakan integrasi kampanye tumbler dalam dunia pendidikan, di mana sekolah memiliki kesempatan untuk melibatkan siswa sebagai agen perubahan. Melalui edukasi yang menyeluruh, siswa dapat memahami dampak besar dari perubahan kecil yang mereka lakukan, seperti beralih menggunakan tumbler. Namun, dampak kampanye ini belum dapat diukur secara menyeluruh karena hanya melibatkan dua kelas (X-XI). Agar kegiatan ini dapat berkelanjutan, partisipasi aktif dari guru sangat diperlukan. Kampanye selanjutnya dapat melibatkan orang tua dan masyarakat sekitar sekolah, serta menggunakan konten visual yang menarik untuk menyampaikan pesan lebih efektif.
Towards Human-machine Collaboration in Autonomous Material Handling on Construction Sites
Authors: Oraskari Jyrki , Kirner Lucas , Zöcklein Marit , Brell-Cokcan Sigrid
Published: December 2024

In the contemporary construction industry, the shortage of skilled labor has prompted the exploration of automation as a remedy and machine autonomy as a potential solution to the environmental conditions at the site. This research explores the balance between human oversight and the independent decision-making capabilities of robots for material delivery on construction sites. Using a scenario-based approach, autonomy in construction robotics is evaluated across four human-machine interaction cases with spatial and temporal dimensions. The expected outcomes revolve around improved safety through better human-machine communication and establishing an interoperable data model to enhance robot autonomy. This aims to automate tasks in challenging construction settings, addressing the shortage of skilled workers and expanding opportunities for individuals capable of meeting the physical demands of these environments.
Autonomy and Agency in Farming: Exploring Human, Machine, and Animal Dynamics
Authors: Drüeke Ricarda , Peil Corinna
Published: December 2024

Human-Machine Communication is reshaping the agricultural sector as digital technologies and automated systems transform farms into complex communicative environments. Farms, which serve as both workplaces and living spaces, uniquely integrate these technologies, yet their impact on farmers’ daily practices, labor, and decision-making remains underexplored. Through an ethnographic approach involving qualitative interviews and farm visits in Austria and southern Germany, the study investigates the interactions among farmers, automated technologies, smart devices, and animals. Using Actor-Network Theory, the redistribution of labor and agency in smart farming is explored. This analysis examines two forms of human-machine relations: the dynamics of automation and autonomy in dairy farming and on a chicken farm, highlighting how these negotiation processes unfold. The findings contribute to a deeper understanding of human-machine interaction, particularly in the context of agriculture’s digital transformation, and provide perspectives on the evolving nature of agricultural work.
Concurrent Plasma Cell Mastitis and Metastatic Breast Cancer a Rare Presentation of a Bilateral Disease: Case Report and Literature Review
Authors: Udosen Joseph E. , Ashindoitiang John A. , Nwagbara Victor I.C. , Ugbem Theophilus I. , Ukam Joseph S. , Asuquo Maurice E.
Published: December 2024

The concurrent occurrence of plasma cell mastitis(PCM) and breast cancer is rare. Plasma cell mastitis(PCM) is a chronic inflammatory breast disease. Metastatic breast cancer(MBC) is breast cancer that has spread to another part of the body commonly the bones, lungs, liver or brain. Presented is a 28 year old woman who was seen at the emergency department with difficulty in breathing and abdominal swelling of one month and two weeks duration respectively. In the previous year, she was diagnosed with bilateral PCM but declined therapy. Clinical, imaging and laboratory evaluation revealed a definitive diagnosis of metastatic bilateral breast cancer. Reported is a bilateral PCM and a metastatic bilateral invasive ductal carcinoma(IDC). PCM should be regarded as a key alert for a persistent check for cancer. Timely diagnosis and prompt surgical removal is recommended for the best possible outcomes.
AI-powered Paralichthys olivaceus Disease Symptom Detection and Classification Method
Authors:   Korean Institute of Smart Media, Jeong Jong Ho , Kim Chan Jin , Choi Han Suk , Jang Hyun Suk , Son Hyun Seung , Lim Han Kyu
Published: December 2024

In this study, we propose a deep learning-based disease symptom detection and machine learning-based disease classification technique to quickly diagnose Paralichthys olivaceus diseases. The disease symptom detection technique, using YOLOv9 model, showed excellent accuracy of about 85.4% of mAP and 75.2% of F1-score. The disease classification, an ensemble model using SGD classifier and random forest classifier was used, and the overall good correct answer rate was about 72% for Top-1, 83% for Top-2, and 94% for Top-3. As a result of testing by combining the two algorithms, when the threshold was not set in the disease classification result, it showed good performance of 96%. The Paralichthys olivaceus disease symptom detection and classification technique proposed in this study can take quick and effective disease treatment and prevention measures by the two algorithms working together to accurately detect disease symptoms and classify disease information.
PENERAPAN BIMBINGAN KELOMPOK DENGAN TEKNIK COGNITIVE RESTRUCKTURING DALAM MENGATASI PROKRASTINASI AKADEMIK SANTRI DI PONDOK PESANTREN AR RAHMAN
Authors: Ilham  , Kusnadi  , Neni noviza
Published: December 2024

This research is entitled "Application of Group Guidance Using Cognitive Restructuring Techniques in Overcoming Academic Procrastination of Palembang Islamic Boarding School Students". This study aims to find out the description of students' academic procrastination and to find out the application of cognitive restructuring techniques in overcoming procrastination at the Ar Rahman Islamic boarding school. This type of research is field research, with a qualitative approach. The subjects of this study were "K" clients, "FW" clients, "NP" clients, "A" clients, and "AKT" clients. Data collection techniques used are observation, interviews, and documentation. While data analysis techniques, data reduction, data presentation, and verification or conclusions. The results of this study indicate that, the picture of procrastination seen from the aspect of not managing time, four subjects are not managing time, only the "FW" client can manage time. From the low self-confidence aspect, four subjects had low self-confidence, only the "FW" client had high self-confidence. From the aspect of considering themselves too busy, four subjects considered themselves too busy, only the "NP" client did not consider himself too busy. From the aspect of stubbornness, the five subjects have stubbornness. From the aspect of manipulating other people's behavior, the five subjects manipulated the behavior of other people. From the aspect of delay as a means of protection for themselves, the five subjects postpone something as a protection for themselves. From the aspect of feeling themselves as victims, the five subjects felt that they were the only ones who were unable to do something. The application of group guidance with cognitive restructuring techniques uses four stages, namely the formation stage, the transition stage, the activity stage, and the termination stage, so that results are obtained, the five clients have experienced a lot of changes, where the five clients can manage time, have higher self-confidence, do not consider self is too busy, not stubborn, does not manipulate the behavior of others, does not use procrastination as a protection to avoid pressure, and does not feel like a victim.
THE INFLUENCE OF FORGIVENESS AND DEMOCRATIC PARENTING STYLE ON BULLYING BEHAVIOR IN YOGYAKARTA HIGH SCHOOL STUDENTS
Authors: Baroroh Nurin , Nana Pramudita , Khoirunnisa Nikki , Putri
Published: December 2024

The purpose this study  to examine the influence of forgiveness and authoritative (democratic) parenting style on the behavior of bullying in high school students Y in Yogyakarta grade 11.  This study uses quantitative approach to test the hypotheses. Result after screening for receive student with authoritative parenting style are 106 people. Data collection is done by using three types of scale, namely the scale of forgiveness, parenting scale, and the scale of bullying behavior. Democratic parenting contribute to effective contribution of 19% and 48% forgiveness of these results indicates the tendency of low bullying behavior to be gained is greatly influenced by forgiveness.
UNDERSTANDING TEACHERS' MOTIVATION: PROFILES OF EARLY CHILDHOOD TEACHERS CONTINUING THEIR STUDIES
Authors: Aisyatin Kamila  , Sa’diah Halimatus , Hidayaturrochman Rizki
Published: December 2024

This study aims to examine and analyze the motivation of early childhood education (ECE) teachers in pursuing higher education. The research method employed is descriptive qualitative, involving students from semesters 1 to 7 who meet the criteria of being ECE teachers and continuing their studies in the PIAUD Department at Ibrahimy University Situbondo. Data collection was carried out using three methods: interviews, observations, and document studies. Subsequently, the researcher analyzed the collected data through three stages: data reduction, data presentation, and conclusion drawing. After conducting the research steps mentioned above, the researcher concluded that the dominant motivation for teachers to continue their studies is primarily driven by external factors, such as educational alignment, institutional demands, and improving their quality of life. Internal motivations include enhancing personal quality as educators and support from their families. Lastly, the university's easy learning system and affordable tuition fees also contribute as motivational factors.
Keywords: Motivation, Learning, ECE Teachers
POLA KOMUNIKASI DALAM PEMBELAJARAN AKHLAK STUDI KASUS ASRAMA AL-HIKMAH PP DARUSSALAM BLOKAGUNG
Authors: Iskandar Hasyim , Fahris Fahris
Published: December 2024

Good communication is a central aspect in the learning and teaching process. This article aims to highlight how the communication pattern in moral learning in the Al-Hikmah dormitory is. Moral learning itself is basically the formation of the character of students. The results showed that the communication pattern of moral learning used was the book TALIMUL MUTAALIM as the medium, and applied the direct learning communication pattern, namely between teachers and students and the indirect learning communication pattern, namely coordination between the teacher and the dormitory administrator.
HUBUNGAN ASUPAN PROTEIN DENGAN TEKANAN DARAH PADA REMAJA DI SMAS AL-MANAR MEDAN JOHOR
Authors: Mohammad Atha Nabil  , Lestari Ira Cinta
Published: December 2024

Asupan protein berperan penting untuk pertumbuhan pada masa remaja. Asupan protein yang tidak seimbang dapat mengakibatkan masalah gizi seperti status protein kurang atau lebih pada remaja, status asupan gizi yang tidak seimbang dapat menyebabkan berbagai gangguan terutama gangguan sirkulasi tekanan darah. Tujuan penelitian ini untuk mengetahui apakah terdapat hubungan asupan protein dengan tekanan darah pada remaja di SMAS Al-Manar Medan Johor. Penelitian bersifat analitik cross sectional. Jumlah sampel penelitian 45 orang yang diambil menggunakan metode non-probability sampling. Data terkumpul merupakan data primer hasil kuesioner. Analisis univariat dan bivariat menggunakan uji korelasi Sommers’d. Hasil penelitian dari 45 sampel berjenis kelamin laki-laki dengan asupan protein kurang dari angka kecukupan gizi (15,6%), asupan protein besar atau sama dengan angka kecukupan gizi (84,4%), tekanan darah hipotensi (8,9%), tekanan darah normal (75,6%), tekanan darah hipertensi (15,6%). Hasil uji korelasi didapati p value = 0,005 (p<0,05) dan r=-0,248 dengan kesimpulan terdapat hubungan yang signifikan antara asupan protein dengan tekanan darah pada remaja di SMAS Al-Manar Medan Johor.
HUBUNGAN KANDUNGAN AKTIF INSEKTISIDA DENGAN KEJADIAN DEMAM BERDARAH DI WILAYAH KERJA PUSKESMAS TANJUNG MORAWA
Authors: Sarirah Munauwarus , Kurnia Madani Azrianur
Published: December 2024

Demam Berdarah Dengue (DBD) adalah penyakit yang ditularkan oleh nyamuk dari genus Aedes, terutama Aedes aegypti dan Aedes albopictus, dan dapat terjadi sepanjang tahun. Penyakit ini seringkali dipengaruhi oleh faktor lingkungan, iklim, kepadatan penduduk, serta perilaku masyarakat. Di Indonesia, kasus DBD mencapai 108.303 pada tahun 2020, dengan Sumatera Utara sebagai salah satu daerah dengan kasus tinggi. Upaya pengendalian DBD sering kali bergantung pada penggunaan insektisida untuk membunuh nyamuk vektor. Penelitian ini bertujuan untuk mengetahui kandungan aktif yang digunakan oleh masyarakat di wilayah kerja Puskesmas Tanjung Morawa serta hubungannya dengan kejadian DBD di wilayah tersebut. Penelitian observasional dengan desain potong lintang ini melibatkan 100 rumah tangga. Data penggunaan insektisida dan riwayat menderita DBD dalam enam bulan terakhir didapatkan melalui wawancara tertulis menggunakan kuesioner. Sebanyak 44 rumah tangga yang diobservasi memiliki riwayat menderita DBD. Seluruh rumah tangga di wilayah kerja Puskesmas Tanjung Morawa menggunakan insektisida golongan piretroid, namun memiliki kandungan aktif yang berbeda-beda yaitu dimeflutrin (43%), sipermetrin (29%) dan praletrin (28%). Terdapat hubungan antara kandungan aktif insektisida dengan kejadian DBD di wilayah Puskesmas Tanjung Morawa.
HUBUNGAN INFEKSI SALURAN PERNAFASAN AKUT BAGIAN ATAS DENGAN OTITIS MEDIA AKUT PADA ANAK
Authors: Simbolon Aditiya Yuda Perkasa Alam
Published: December 2024

Otitis media akut (OMA) merupakan penyakit kedua tersering pada masa kanak-kanak setelah infeksi saluran pernapasan Akut (ISPA) bagian atas. Prevalensi OMA di setiap negara bervariasi, berkisar antara 2,3 - 20%. OMA berhubungan dengan ISPA bagian atas, dimana 94% pasien penderita ISPA bagian atas akan menjadi OMA. Satu dari tiga anak yang terserang ISPA bagian atas akan mengalami OMA setelahnya. Penelitian ini bertujuan untuk mengetahui hubungan ISPA bagian atas sebagai faktor predisposisi terjadinya OMA. Penelitian ini menggunakan studi observasi dalam bentuk analitik dengan desain potong lintang. Pemilihan subjek penelitian dilakukan secara non probability consecutive sampling. Penderita ISPA bagian atas dan non ISPA bagian atas yang memenuhi kriteria inklusi dan eksklusi akan dinilai hubungnnya dengan kejadian OMA. Dari 42 orang subjek yang mengalami ISPA bagian atas terdapat 31 orang (73,8%) dengan OMA, sementara itu dari 42 orang subjek tanpa ISPA bagian atas terdapat 7 orang (16,7%) dengan OMA. Hal ini menunjukkan bahwa terdapat hubungan yang signifikan secara statistik antara ISPA bagian atas dan OMA (p<0,05). Kesimpulan penelitian ini adalah adanya hubungan antara ISPA bagian atas dan OMA pada anak.
DAMPAK CYBERBULLYING TERHADAP NON-SUICIDAL SELF INJURY: SLR
Authors: SALSABILUL JANNAH SAFIRA
Published: December 2024

Perbuatan yang tak jarang ditemukan dan dapat mengancam nyawa di era modern ini adalah cyberbullying. Hal ini dikarenakan tekanan yang didapatkan dari tindakan cyberbullying yang membuat seseorang merasa tidak nyaman dengan dirinya. Seseorang yang mengalami cyberbullying cenderung tidak percaya diri sehingga mudah sekali untuk terprovokasi oleh hal negatif salah satunya dengan melakukan Nonsuicidal self injury. Metode yang dipilih untuk penelitian ini adalah systematic literature review dengan menggunakan PRISMA sebagai alat untuk panduan penilaian artikel. Jumlah artikel akhir yang sesuai dengan kriteria inklusi sejumlah 8 artikel. Pada hasil tinjauan sistematis yang telah dilakukan bahwa semua penelitian tersebut menyatakan terdapat korelasi positif antara cyberbullying atau cybervictimization dengan NSSI. Hal ini sejalan dengan dampak yang dipaparkan pada beberapa penelitian bahwa faktor penyebab paling sering orang melakukan NSSI adalah disebabkan karena adanya perilaku dan tekanan dari cyberbullying atau cybervictimization. Penelitian lain juga mendukung dan dibuktikan bahwa terdapat korelasi positif antara cyberbullying dengan NSSI pada remaja.
Penegakan Hukum terhadap Supporter Sepak Bola dalam Tragedi Kanjuruan Ditinjau dari Perspektif Hak Asasi Manusia
Authors: Nafi'ah Luluk , Khuza Muhammad Diwani Adil
Published: December 2024

Penilitian ini bertujuan untuk mengetahui Hak Asasi Manusia dalam tyragedi sepak bola kenjuruhan di indoneisa. Dunia persepakbolaan Indonesia memiliki tragedi kelam tepatnya tanggal 1 oktober 2022. Setelah terjadinya pertandingan sepak bola antara arema dengan rivalnya persebaya. Insiden tersebut memakan korban kurang lebih 132 orang suporter Arema Malang meninggal dunia dan 580 orang terluka. Jurnal ini menggunakan metode penelitian hukum normatif dengan pendekatan studi kasus. Tragedi kemanusiaan di Kanjuruhan merupakan kasus pelanggaran hak asasi manusia yang terjadi karena prosedur tidak sesuai, tidak menghormati dan menjamin prinsip dan keamanan organisasi sepak bola. Komnas HAM mengumumkan PT Liga Indonesia Baru selaku penyelenggara dan penyiar Liga 1 mengabaikan aspek keamanan pertandingan Arema dengan Persebaya Surabaya yang menyebabkan tragedi Kanjuruhan.
Penggunaan Bom Nuklir Tipe Atomic Bomb, A-Booms pada Perang Dunia II dalam Perspektif Hukum Humaniter
Authors: Firmansyah Muhammad Afifudin
Published: December 2024

Penelitian ini bertujuan untuk mengetahui pandangan hukum humaniter internasional tentang penggunaan bom serta larangan dan implikasi penggunaan bom nuklir pada perang dunia kedua. Senjata nuklir merupakan suatu bentuk perkembangan teknologi persenjataan dengan menghadirkan senjata yang memiliki kekuatan sangat besar. Penggunaan alat persenjataan dalam konflik bersenjata seringkali menimbulkan permasalahan hukum, terutama terhadap alat persenjataan yang terlarang. Alat persenjataan terlarang digunakan oleh Amerika Serikat dalam perang dunia kedua terbukti merugikan bagi kesehatan dan kehidupan di sekitar daerah yang menjadi sasaran. Metode penelitian ini penelitian normatif dengan berlandaskan teori yaitu Protokol Jenewa 1925, Konvensi Den Haag 1907, Protokol Tambahan 1 tahun 1977, Konvensi tahun 1972 tentang dan Konvensi tahun 1993. Hasil dari penelitian ini menandakan bahwa hukum humaniter menolak keras penggunaan bom atom pada perang dunia kedua dikarenakan menyebabkan kerugian yang massif dan tidak perlu serta adanya batasan penggunaan bertujuan untuk memanusiakan perang itu sendiri.
Perlakuan Tawanan Perang Dalam Hukum Islam Dan Hukum Humaniter Internasional
Authors: Lahirriyah Khanida Tetty Nur , Yulianti Firdaul Eka
Published: December 2024

Perang sebagaimana dijelaskan oleh Clausewitz, merupakan tindakan kekerasan yang digunakan oleh satu negara untuk memaksakan kehendaknya pada negara lain. Salah satu aspek yang sering menjadi perdebatan dalam konteks perang adalah perlakuan trrhadap tawanan perang. Tawanan perang adalah individu yang ditangkap dan ditahan oleh pihak lawan dalam situasi konflik bersenjata. Isu perlakuan terhadap tawanan perang ini diatur oleh dua kerangka hukum utama: Hukum Islam dan Hukum Humaniter Internasional (HHI). Keduanya memiliki prinsip dan aturan yang bertujuan untuk memastikan perlakuan yang manusiawi terhadap tawanan perang, meskipun terdapat perbedaan dalam asas, ruang lingkup, dan mekanisme penegakannya. Penelitian ini bertujuan untuk menganalisis perbedaan dan kesamaan antara Hukum Islam dan HHI dalam mengatur perlakuan terhadap tawanan perang, serta mengeksplorasi tantangan dalam harmonisasi kedua kerangka hukum ini. Metode yang digunakan adalah yuridis normatif dengan pendekatan studi kepustakaan untuk menelaah aturan hukum, prinsip-prinsip hukum, serta doktrin-doktrin hukum yang relevan. Hasil penelitian menunjukkan bahwa meskipun Hukum Islam dan HHI memiliki tujuan yang sama dalam melindungi tawanan perang, terdapat perbedaan signifikan dalam pengaturan dan penerapannya. Hukum Islam lebih bersifat global dan memerlukan penafsiran lebih lanjut, sementara HHI memiliki aturan yang lebih rinci dan sistematis. Perbedaan ini menciptakan tantangan dalam harmonisasi kedua kerangka hukum, terutama dalam konteks penerapan prinsip-prinsip perlakuan yang adil dan manusiawi. Implikasi dari penelitian ini adanya perlunya upaya lebih lanjut untuk mengembangkan kerangka hukum yang dapat menjembatani perbedaan antara Hukum Islam dan HHI, guna memastikan perlakuan yang adil dan manusiawi terhadap tawanan perang di seluruh dunia.
Analisis Legal Reasoning dan Dampak Putusan Mahkamah Konstitusi Nomor 90/PUU-XXI/2023
Authors: Nugraha Kristiawan Putra , Puspitasari Dela , Anggraini Riska
Published: December 2024

Putusan Mahkamah Konstitusi Nomor 90/PUU-XXI/2023 memuat adanya konflik kepentingan dalam pengambilan Keputusan yang mengakibatkan hakim ketua Anwar Usman diberhentikan dari jabatannya sebagai ketua Mahkamah Konstitusi, dengan melalui putusan Majelis Kehormatan Mahkamah Konstitusi Nomor 02/MKMK/L/11/2023 telah membuktikan adanya pelanggaran serius terhadap kode etik dan prilaku hakim konstitusi yang dilakukan oleh Anwar Usman. Artikel ini meneliti akar permasalahan legal reasoning dalam putusan Mahkamah Konstitusi Nomor 90/PUU-XXI/2023 dengan mengkaji secara komprehensif serta dampak setelah putusan tersebut dikeluarkan. Pengumpulan data dilakukan melalui studi kepustakaan dengan melibatkan sumber sekunder seperti buku, makalah, dan artikel. Pendekatan konseptual, perundang-undangan, dan perbandingan digunakan untuk menganalisis bahan hukum, dengan fokus pada pola pikir yang relevan dengan isu hukum yang diteliti. Hasil analisis menyoroti adanya pelanggaran kode etik terkait prinsip ketidakberpihakan, integritas, kecakapan, kesetaraan, independensi, dan kepantasan yang dilakukan oleh hakim Anwar Usman, sebagaimana aturan tersebut telah diatur dalam Undang-Undang Nomor 48 Tahun 2009 tentang Kekuasaan Kehakiman. Meskipun demikian, penelitian juga menyoroti komponen keberhasilan hukum sendiri sangat dipengaruhi oleh tiga komponen utama seperti komponen struktural, komponen substansial, dan komponen kultural. Penelitian ini memiliki implikasi penting baik dalam ranah akademik, praktik,maupun kebijakan publik. Pertama dari segi akademik, penelitian ini menjadi referensi bagi studi hukum tata negara dan konstitusionalisme di masa depan, kedua dalam praktik hukum, penelitian ini memberikan kontribusi signifikan dalam memahami dan mengaplikasikan terkait prinsip prinsip legal reasoning yang lebih konsisten dan transparan, ketiga dari perspektif kebijakan publik, penelitian.
Characterizing Hardware Utilization on Edge Devices when Inferring Compressed Deep Learning Models
Authors: Nabhaan Ahmad Naufal Labiib , Rachmanto Rakandhiya Daanii , Setyanto Arief
Published: December 2024

Implementing edge AI involves running AI algorithms near the sensors. Deep Learning (DL) Model has successfully tackled image classification tasks with remarkable performance. However, their requirements for huge computing resources hinder the implementation of edge devices. Compressing the model is an essential task to allow the implementation of the DL model on edge devices. Post-training quantization (PTQ) is a compression technique that reduces the bit representation of the model weight parameters. This study looks at the impact of memory allocation on the latency of compressed DL models on Raspberry Pi 4 Model B (RPi4B) and NVIDIA Jetson Nano (J. Nano). This  research aims to understand hardware utilization in central processing units (CPU), graphics processing units (GPU),and memory. This study focused on the quantitative method, which controls memory allocation and measures warm-up time, latency, CPU, and GPU utilization. Speed comparison among inference of DL models on RPi4B and J. Nano. This paper observes the correlation between hardware utilization versus the various DL inference latencies. According to our experiment, we concluded that smaller memory allocation led to high latency on both RPi4B and J. Nano. CPU utilization on RPi4B. CPU utilization in RPi4B increases along with the memory allocation; however, the opposite is shown on J. Nano since the GPU carries out the main computation on the device. Regarding computation, thesmaller DL Size and smaller bit representation lead to faster inference (low latency), while bigger bit representation on the same DL model leads to higher latency.
Variation of Distributed Power Control Algorithm in Co-Tier Femtocell Network
Authors: Harahap Fatur Rahman , Isnawati Anggun Fitrian , Ni'amah Khoirun
Published: December 2024

The wireless communication network has seen rapid growth, especially with the widespread use of smartphones, but resources are increasingly limited, especially indoors. Femtocell, a spectrum-efficient small cellular network solution, faces challenges in distributed power control (DPC) when deployed with distributed users, impacting power levels, and causing interference in the main network. The aim of this research is optimizing user power consumption in co-tier femtocell networks by using the user power treatment. This study proposed the Distributed Power Control (DPC) variation methods such as Distributed Constrained Power Control (DCPC), Half Distributed Constrained Power Control (HDCPC), and Generalized Distributed Constrained Power Control (GDCPC) in co-tier femtocell network. The research examines scenarios where user power converges but exceeds the maximum threshold or remains semi-feasible, considering factors like number of users, distance, channel usage, maximum power values, non-negative power vectors, Signal-to-Interference-plus-Noise Ratio (SINR), and link gain matrix values. In Distributed Power Control (DPC), distance and channel utilization affect feasibility conditions: feasible, semi-feasible, and non-feasible. The result shows that Half Distributed Constrained Power Control (HDCPC) is more effective than Distributed Constrained Power Control (DCPC) in semi-feasible conditions due to its efficient power usage and similar Signal-to-Interference-plus-Noise Ratio (SINR). Half Distributed Constrained Power Control (HDCPC) is also easier to implement than Generalized Distributed Constrained Power Control (GDCPC) as it does not require user deactivation when exceeding the maximum power limit. Distributed Power Control (DPC) variations can shift the power and Signal-to-Interference-plus-Noise Ratio (SINR) conditions from non-convergence to convergence at or below the maximum power level. We concluded that the best performance of Distributed Power Control (DPC) is Half Distributed Constrained Power Control (HDCPC).
Implementation of The Extreme Gradient Boosting Algorithm with Hyperparameter Tuning in Celiac Disease Classification
Authors: Alfirdausy Roudlotul Jannah , Ulinnuha Nurissaidah , Utami Wika Dianita
Published: December 2024

Celiac Disease (CeD) is an autoimmune disorder triggered by gluten consumption and involves the immune system and HLA in the intestine. The global incidence ranges from 0.5%-1%, with only 30% correctly diagnosed. Diagnosis remains challenging, requiring complex tests like blood tests, small bowel biopsy, and elimination of gluten from the diet. Therefore, a faster and more efficient alternative is needed. Extreme Gradient Boosting (XGBoost), an ensemble machine learning technique that utilizes decision trees to aid in the classification of Celiac disease, was used. The aim of this study was to classify patients into six classes, namely potential, atypical, silent, typical, latent and none disease, based on attributes such as blood test results, clinical symptoms and medical history. This research method employs 5-fold cross-validation to optimize parameters that are max depth, n estimator, gamma, and learning rate. Experiments were conducted 96 times to get the best combination of parameters. The results of this research are highlighted by an improvement of 0.45% above the accuracy value with the default XGBoost parameter of 98.19%. The best model was obtained in the trial with parameters max depth of 3, n estimator of 100, gamma of 0, and learning rate of 0.3 and 0.5 after modifying the parameters, yielding an accuracy rate of 98.64%, a sensitivity rate of 98.43%, and a specificity rate of 99.72%. This research shows that tuning the XGBoost parameters for Celiac
Integration of Deep Learning and Autoregressive Models for Marine Data Prediction
Authors: Fadhli Mukhlis , Jaya Indra , Nurdianti Sri , Priandana Karlisa , Hermadi Irman
Published: December 2024

Climate change and human activities significantly affect the dynamics of the marine environment, making accurate predictions essential for resource management and disaster mitigation. Deep learning models such as Long Short-Term Memory excel at capturing non-linear temporal patterns, while autoregressive models handle linear trends to improve prediction accuracy. This aim study predicts sea surface temperature, height, and salinity using deep learning compared to Moving Average and Autoregressive Integrated Moving Average methods. The research methods include spatial gap analysis, temporal variability modeling, and oceanographic parameter prediction. The relationship betweenparameters is analyzed using the Pearson Correlation method. The dataset is divided into 80% training and 20% test data, with prediction results compared between Long Short-Term Memory, Moving Average, and Autoregressive models. The results show that Long Short-Term Memory performs best with a Root Mean Squared Error of 0.1096 and a Mean Absolute Error of 0.0982 for salinity at 13 sample points. In contrast, Autoregressive models produce a Root Mean Squared Error of 0.193 for salinity, 0.055 for sea surface height, and 2.504 for sea surface temperature, with a correlation coefficient 0.6 between temperature and sea surface height. In conclusion, the Long Short Term Memory model excels in predicting salinity because it is able to capture complex non-linear patterns. Meanwhile, Autoregressive models are more suitable for linear data trends and explain the relationship between parameters, although their accuracy is lower in salinity prediction. This approach
Optimizing Currency Circulation Forecasts in Indonesia: A Hybrid Prophet- Long Short Term Memory Model with Hyperparameter Tuning
Authors: Aziza Vivin Nur , Syafitri Utami Dyah , Fitrianto Anwar
Published: December 2024

The core problem for decision-makers lies in selecting an effective forecasting method, particularly when faced with the challenges of nonlinearity and nonstationarity in time series data. To address this, hybrid models are increasingly employed to enhance forecasting accuracy. In Indonesia and other Muslim countries, monthly economic and business time series data often include trends, seasonality, and calendar variations. This study compares the performance of the hybrid Prophet-Long Short-Term Memory (LSTM) model with their individual counterparts to forecast such patterned time series. The aim is to identify the best model through a hybrid approach for forecasting time series data exhibitingtrend, seasonality, and calendar variations, using the real-life case of currency circulation in South Sulawesi. The goodness of the models is evaluated using the smallest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The results indicate that the hybrid Prophet- LSTM model demonstrates superior accuracy, especially for predicting currency outflow, with lower MAPE and RMSE values than standalone models. The LSTM model shows excellent performance for currency inflow, while the Prophet model lags in inflow and outflow accuracy. This insight is valuable for Bank Indonesia’s strategic planning, aiding in better cash flow prediction and currency stock management.
Population Prediction Using Multiple Regression and Geometry Models Based on Demographic Data
Authors: Safii M , Setiana Rika
Published: December 2024

Population growth is an important issue because it significantly impacts a country’s growth and development. Large population growth can impact potential resources that drive the pace of the economy and national development. On the other hand, it can also be a problem of poverty, hunger, unemployment, education, health, and others. The government needs to control population growth to balance it with good population quality. Data sourced from the Population and Civil Registration Office of Simalungun Regency, Tanah Java sub-district has a high population and continues to increase every year. The impact of the population increase is that it affects the population’s welfare, most of whom work as laborers and farmers. To overcome this problem, it is necessary to predict the number of people in the future so that the government can make the right decisions and policies in controlling the population. This study aims to make predictions using two models, namely Multiple Linear Regression, to find linear equations and Geometry Models for population growth projections. This study utilizes multiple regression analysis and geometric models using three independent variables, namely birth rate (X1), migration rate (X2), and death rate (X3), as well as one bound variable, population number (Y). This study’s results show that the Tanah Java sub-district population is expected to increase in the next five years (2024-2028). Predictions show that by 2024, the population is expected to reach 61178 people from 59589 in 2023. Based on the results of the study, the conclusion of this study it can be used as a guide for the authorities in planning strategies and resource allocation and making a significant contribution in estimating population development in the Java region so that there will be no population explosion in the future so that it does not have a negative impact.
Segmentation and Classification of Breast Cancer Histopathological Image Utilizing U-Net and Transfer Learning ResNet50
Authors: Sudianjaya Nella Rosa , Fatichah Chastine
Published: December 2024

Breast cancer is the most common type of cancer among various types of cancer. Approximately 1 in 8 women in the United States die from breast cancer. Early screening and accurate diagnosis are essential for prevention and accelerated treatment intervention. Several artificial intelligence methods have emerged to develop effective segmentation, detection, and classification to determine cancer types. Although there has been progress in automated algorithms for breast cancer histopathology image analysis, many of these approaches still face several challenges. This study aims to address the challenges in breast cancer image analysis. This research method uses the development of the U-Net architecture combined with Transfer Learning using ResNet50. The encoder path aims to improve the model’s sensitivity in the segmentation and classification of cancer areas by utilizing deep hierarchical features extracted by ResNet50. In addition, data augmentation techniques are used to create a diverse and comprehensive training dataset, which improves the model’s ability to distinguish between different tissue types and cancer areas. The results of this study are U-Net and ResNet50, which show an average IoU of 0.482 and a Dice coefficient of 0.916. This study concludes that integrating UNet with Transfer Learning ResNet50 improves the segmentation and classification accuracy in breast cancer histopathology images and overcomes the problem of high computational requirements. This approach shows significant potential for improvement in early breast cancer detection and diagnosis.
Higher Education Institution Clustering Based on Key Performance Indicators using Quartile Binning Method
Authors: Fatmawaty Virdiana Sriviana , Riadi Imam , Herman Herman
Published: December 2024

The Key Performance Indicators of Higher Education Institutions (KPI-HEIs) are a crucial component of the internal quality assurance system that supports the achievement of excellence status for higher education institutions. Many private higher education institutions face challenges in independently analyzing the key performance assessment indicators of Private Higher Education Institutions (PHEIs), which often require complex methodological approaches and specialized expertise. The research aims to cluster PHEIs based on achieving key performance indicators (KPIs). Research the method used descriptive statistical methods and quartile binning techniques to analyze and cluster data based on the achievement of KPI-HEIs. The research results, based on descriptive statistical analysis, identified outliers in eight KPI-HEIs, along with a dominance of zero values in KPI 1, KPI 2, KPI 6, KPI 7, and KPI 8, with the highest proportion reaching 90.91% for KPI 8. Based on these findings, clustering using the quartile binning method resulted in four clusters of PHEIs based on KPIs: Cluster 1 consists of 19 institutions with poor, Cluster 2 consists of 14 institutions with fair achievement, Cluster 3 consists of 16 institutions with good achievement, and Cluster 4 consists of 17 institutions with very good achievement, which can serve as examples for other institutions. This research concludes that the quartile binning method successfully categorized private higher education institutions based on their achievement of KPIs into four clusters: poor, fair, good, and very good. This outcome demonstrates the effectiveness of the method in understanding the performance distribution of these institutions. It provides valuable insights for stakeholders to develop data-driven strategies aimed at enhancing educational quality.
Optimizing Hotel Room Occupancy Prediction Using an Enhanced Linear Regression Algorithms
Authors: Pramita Dewa Ayu Kadek , Saraswati Ni Wayan Sumartini , Sandana I Putu Dedy , Pirozmand Poria , Bisena I Kadek Agus
Published: December 2024

Predicting the correct hotel occupancy rate is important in the tourism industry because it has a major impact on the level of revenue and maintenance of a hotel’s reputation. With accurate predictions, hotel performance can be optimized regarding resources, staff, and hotel facilities. The linear regression method has been proven to perform causal predictions well. However, this method has several weaknesses, such as the function of the relationship between dependent variables and independent variables that are not linear, overfitting, or underfitting in building the prediction model. The purpose of this study was to optimize the linear regression model in predicting hotel occupancy rates. The method used in this study was a Linear Regression method optimized with Polynomial Regression and regularization techniques to reduce overfitting using Ridge Regression and Lasso Regression. The results of the model evaluation showed that linear regression, which was optimized with Polynomial Regression and Ridge Regression in the model with the historical data of the Adiwana Unagi occupancy rate, historical data of the hotel occupancy rate in Bali, and the number of tourist visits in Bali, gave the best performance, with a mean absolute error score of 1.0648, root mean square error of 2.1036, and R-squared of 0.9953. The conclusion of this research was optimization using polynomial regression, achieving the best evaluation scores, where the prediction model performance indicates that variable X7 (tourist visit numbers) strongly influences the prediction of the occupancy rate.
Multi-Algorithm Approach to Enhancing Social Assistance Efficiency Through Accurate Poverty Classification
Authors: Satria Christofer , Sugijanto Peter Wijaya , Anggrawan Anthony , Sumadewa I Nyoman Yoga , Dayani Aprilia Dwi , Anggriani Rini
Published: December 2024

The determination of poverty status in Lombok Utara district depends on criteria such as income, access to health and education services, and housing conditions. These factors are crucial for assessing the level of community welfare and guiding the allocation of social assistance by the district government. The purpose of this study is to address the gap by utilizing advanced data mining techniques to improve the accuracy of poverty status classification in North Lombok, thereby informing more effective social assistance policies. The method used in this research is the Random Forest (RF), K-Nearest Neighbor (KNN) and Naïve Bayes with split data 80% data training and 20% data testing. The finding indicated that the machine learning model the RF algorithm, which achieved an accuracy rate of 82.56%, proved to play an important role in this process by effectively distinguishing between different categories of poverty based on these criteria. In comparison, the KNN algorithm achieved an accuracy of 70.94% and the Naïve Bayes model achieved an accuracy of 53.47%. It means that the machine learning model using the RF algorithm has more accurate accuracy than the KNN and Naïve Bayes algorithm in predicting or recommending Recipients of Social Assistance from the District Government. The implication is that RF machine learning can help the role of social service officers in predicting the economic status of the community. The high accuracy of the RF algorithm enhances its role in informing targeted policy decisions and optimizing the effectiveness of social assistance programs. Nonetheless, continuous improvement is essential to refine the model's predictive capabilities and ensure the accuracy and reliability of poverty assessments. These continuous improvements are essential to effectively alleviate poverty and break the cycle of socio-economic disparities in the region.
Enhancing Multiple Linear Regression with Stacking Ensemble for Dissolved Oxygen Estimation
Authors: Rahmaddeni Rahmaddeni , Wicaksono M. Teguh , Wulandari Denok , Agustriono Agustriono , Ibrahim Sang Adji
Published: December 2024

Maintaining optimal dissolved oxygen levels is essential for aquatic ecosystems, yet industrial and domestic waste has led to a global decline in dissolved oxygen. Traditional measurement methods, such as oxygen meters and Winkler titration, are often costly or time-consuming. This study aims to improve the Root Mean Square Error, Mean Absolute Error, and R2 values for estimating dissolved oxygen levels. The research method uses Multiple Linear Regression with various training and testing data splits, both before and after applying polynomial features. The model is further optimized using a stacking technique, with Random Forest Regressor and Gradient Booster Regressor as base models.The results show that the best model was achieved using the stacking ensemble technique with a 90:10 data split and polynomial features, yielding a Root Mean Square Error of 1.206, Mean Absolute Error of 0.990, and R2 of 0.670. This model has also met the assumptions of linear regression, such as residual normality, homoscedasticity, and no autocorrelation of residuals. This study concluded that the ensemble stacking technique and the addition of polynomial features could improve the model in estimating dissolved oxygen values and also contribute by providing an accessible user interface using the Gradio Framework, allowing users to estimate dissolved oxygen levels effectively.