Institut Teknologi Indonesia

в статье исследуется нелинейная взаимосвязь между уровнем экономического неравенства и долгосрочным экономическим ростом через ключевой трансмиссионный канал – социальную мобильность. Разработана модель перекрывающихся поколений (OLG), в которой способность домохозяйств инвестировать в человеческий капитал (образование и здоровье) потомства напрямую зависит от их текущего дохода и наследуемого социально-экономического статуса.

This study examines the effects of soft skills, motivation, and parental role on students’ work readiness. An explanatory quantitative approach was employed, involving students from the 2021 cohort of the Management Study Program at ITEBIS PGRI Dewantara Jombang. Data were collected through a questionnaire survey and analyzed to determine the relationships among the variables under investigation. The findings indicate that soft skills, motivation, and parental role significantly affect students’ work readiness. Among these variables, motivation emerged as the most dominant factor in explaining work readiness. These findings underscore that students’ work readiness is shaped not only by non-technical competencies, but also by internal drive for self-development and family support in preparing for future careers. Accordingly, this study highlights the importance of strengthening soft skills, enhancing motivation, and encouraging parental involvement as part of higher education strategies to support students’ work readiness prior to graduation.

Food safety requires rapid and accurate bacterial identification to prevent disease and economic losses. This study compares three lightweight deep learning models—Tiny-ViT, ShuffleNetV2, and EfficientNet-Lite—for classifying 33 bacterial species from a combined public dataset. Models were trained using transfer learning with original and augmented data and evaluated using 5-fold cross-validation. Tiny-ViT achieved the highest performance with 99.66% accuracy and 99.70% precision, setting a new state-of-the-art for the DIBaS dataset. EfficientNet-Lite reached 99.32% accuracy with superior efficiency—threefold lower FLOPs (397.49M), fewer parameters (3.41M), and faster inference (0.90 ms/image). Comparison of per-class error rates across four models—Tiny-ViT Original, Tiny-ViT Augmented, EfficientNet-Lite Augmented, and EfficientNet-Lite Original—showed consistent stability, where each bacterial class exhibited low mean error and narrow 95% confidence intervals (CI95%), reflecting statistical reliability. These findings highlight a trade-off: Tiny-ViT offers maximum accuracy, while EfficientNet-Lite provides optimal accuracy–efficiency balance for edge-based bacterial diagnostics.

Pada tahun 2025, Unit Pelaksana Transmisi Duri Kosambi dan Pulogadung bertanggung jawab atas pengoperasian 157 trafo tenaga dan 905 trafo arus di 54 gardu induk. Meningkatkan Keandalan Sistem Transmisi: Dengan memastikan ketersediaan material cadang yang memadai, diharapkan gangguan pada trafo dapat diminimalkan, sehingga meningkatkan keandalan sistem transmisi listrik. Mengoptimalkan Pengelolaan Inventori: Penelitian ini bertujuan untuk membantu Unit Pelaksana Transmisi dalam mengelola inventori material cadang secara efisien, baik dalam hal jumlah maupun biaya yang diperlukan, guna mendukung operasional yang lebih optimal. Analisis Deret Waktu (Time Series Analysis): Untuk memodelkan pola kegagalan dan permintaan material cadang dalam periode tertentu, sehingga dapat memprediksi kebutuhan material di masa mendatang. Pendekatan Keandalan (Reliability Approach): Pendekatan ini digunakan untuk mengukur keandalan sistem berdasarkan tingkat kegagalan dan waktu tunggu material cadang, guna memastikan ketersediaan material yang sesuai dengan kebutuhan yang dihadapi. Trafo Tenaga: 4 unit dengan tingkat kegagalan (λ) sebesar 0,0264 dan waktu pemulihan (β) sebesar 0,3541. Estimasi biaya minimum yang diperlukan adalah sebesar Rp21.345.844.706, dengan tingkat keandalan 94,08%.Trafo Arus: 2 unit dengan biaya minimum sebesar Rp80.463.294 dan tingkat keandalan 96,17%. Hasil penelitian ini diharapkan dapat meningkatkan efisiensi dalam pengelolaan material cadang dan memberikan kontribusi positif terhadap keandalan sistem transmisi. Dengan adanya sistem pengelolaan material cadang yang lebih baik, gangguan akibat kegagalan trafo dapat diminimalkan, dan waktu downtime dapat dikurangi, sehingga operasional gardu induk dapat berlangsung lebih stabil dan efisien.

Low numeracy literacy skills in early childhood, such as difficulty recognizing number symbols and quantity concepts, pose a challenge in preparing for primary education. This study aims to improve the numeracy literacy skills of children aged 5–6 years through the use of Pop-Up Book media. The research method employed was Classroom Action Research (CAR) using the Kemmis and McTaggart model, conducted in two cycles. The subjects were 19 children at KB Kasih Bunda, Indragiri Hilir. Data were collected through observation sheets, documentation, and interviews, then analyzed using descriptive comparative analysis. The results showed a significant increase in each cycle. In the pre-cycle stage, children's average ability ranged from 30%–45%. After the implementation of Pop-Up Book media, the scores increased to 50%–68% in Cycle I and peaked at 80%–90% in Cycle II. Beyond cognitive aspects, children's learning enthusiasm also increased to 85%. It is concluded that Pop-Up Book media is effective in improving early childhood numeracy literacy by providing interactive and concrete visualizations. This media is recommended as an innovative learning tool to support holistic cognitive development in children.

Kinerja turbin gas secara bertahap menurun akibat penumpukan kotoran, stres termal, dan keausan komponen, yang mengakibatkan penurunan efisiensi termal. Perawatan besar-besaran (major overhaul) sangat penting untuk memulihkan kinerja mendekati kondisi desain. Studi ini menganalisis dampak perawatan besar-besaran terhadap efisiensi termal Unit Turbin Gas 1.2 di Pembangkit Listrik Muara Karang, dengan fokus pada operasi beban maksimum sebesar 90 MW. Data operasional aktual diproses menggunakan prinsip termodinamika siklus Brayton untuk menghitung output kerja bersih, masukan energi bahan bakar, dan efisiensi termal keseluruhan. Perhitungan diverifikasi menggunakan Cycle Tempo dan Engineering Equation Solver (EES), dengan penyimpangan kurang dari 5%. Distribusi energi dan kerugian divisualisasikan menggunakan diagram Sankey untuk membandingkan kondisi sebelum dan setelah overhaul. Hasil menunjukkan bahwa efisiensi termal meningkat dari 31,91% menjadi 32,41% setelah overhaul, yang sesuai dengan pengurangan kerugian panas sekitar 2.950 kJ/s. Peningkatan ini terutama terkait dengan pemulihan kompresor dan pengurangan irreversibilitas di pembakar. Temuan ini menunjukkan bahwa overhaul besar merupakan strategi pemeliharaan yang efektif untuk meningkatkan kinerja termal, sementara diagram Sankey memberikan wawasan berharga tentang kerugian energi pada tingkat komponen untuk pengambilan keputusan operasional dan pemeliharaan.

Percepatan digitalisasi belum sepenuhnya diimbangi literasi digital pemuda pedesaan. Desa Carawali, Kecamatan Wattangpulu, Kabupaten Sidenreng Rappang, memiliki potensi melalui Karang Taruna beranggotakan 40 orang. Namun hanya sekitar 30% anggota mampu mengoperasikan komputer, ditambah keterbatasan akses serta metode pelatihan. Kondisi ini menghambat pemberdayaan masyarakat berbasis teknologi. Kegiatan pengabdian ini bertujuan meningkatkan literasi digital dan kewirausahaan digital pemuda desa melalui pemanfaatan teknologi informasi berbasis game edukasi. Metode pelaksanaan meliputi pelatihan interaktif, praktik langsung, dan pendampingan intensif berorientasi wirausaha digital. Evaluasi dilakukan melalui asesmen awal dan akhir untuk mengukur peningkatan kemampuan peserta. Hasil menunjukkan peningkatan signifikan, dengan 80% anggota Karang Taruna mampu mengoperasikan perangkat digital. Temuan ini menjadi dasar perencanaan pendampingan lanjutan guna menjamin keberlanjutan literasi serta penguatan wirausaha digital. Pendekatan inovatif berbasis game edukasi terbukti efektif memperkuat kapasitas sumber daya manusia dan mendukung pembangunan desa berbasis teknologi secara berkelanjutan melalui kolaborasi pemuda, pemerintah desa, dan pemangku kepentingan lokal terkait pendidikan inklusif berkelanjutan.

Sistem arus lemah pada gedung bertingkat berperan strategis dalam mendukung fungsi komunikasi, keamanan, dan keselamatan bangunan. Dibanding bangunan rendah, kompleksitasnya lebih tinggi karena melibatkan distribusi vertikal melalui riser, keterbatasan ruang teknis, kebutuhan redundansi jaringan, serta integrasi berbagai subsistem seperti structured cabling, CCTV, access control, dan fire alarm dalam satu ekosistem operasional, sehingga menuntut perancangan dan implementasi yang terstruktur serta sesuai dengan standar teknis yang berlaku, termasuk Standar Nasional Indonesia (SNI). Penelitian ini bertujuan mengkaji secara sistematis perkembangan riset terkait desain dan implementasi sistem arus lemah pada gedung bertingkat serta mengidentifikasi kecenderungan pendekatan, tantangan teknis, dan celah penelitian. Metode yang digunakan adalah Systematic Literature Review (SLR) mengacu pada pedoman PRISMA, dengan 10 artikel jurnal internasional terbitan 2015–2025 yang diseleksi melalui proses identifikasi, penyaringan, dan evaluasi kelayakan, kemudian dianalisis secara tematik. Hasil kajian menunjukkan pergeseran dari pendekatan konvensional menuju integrasi berbasis Building Information Modeling (BIM), otomasi desain, optimasi jaringan, serta pemanfaatan IoT dan edge computing. Structured cabling teridentifikasi sebagai infrastruktur inti yang menopang integrasi subsistem, sementara keamanan data, manajemen bandwidth, dan integrasi monitoring kebakaran menjadi aspek krusial dalam menjamin keandalan bangunan. Temuan ini menegaskan pentingnya pendekatan integratif berbasis BIM dan dokumentasi digital sejak tahap perencanaan untuk mendukung verifikasi kepatuhan standar serta pengembangan model evaluasi kesesuaian berbasis indikator teknis terukur. Low-voltage systems in high-rise buildings play a strategic role in supporting communication, security, and safety functions. Compared to low-rise buildings, their complexity is significantly higher due to vertical distribution through risers, limited technical spaces, network redundancy requirements, and the integration of multiple subsystems such as structured cabling, CCTV, access control, and fire alarm within a single operational ecosystem, thereby requiring structured design and implementation in compliance with applicable technical standards, including Indonesian National Standards (SNI). This study aims to systematically examine research developments related to the design and implementation of low-voltage systems in high-rise buildings and to identify prevailing approaches, technical challenges, and research gaps. The method employed is a Systematic Literature Review (SLR) based on PRISMA guidelines, analyzing 10 international journal articles published between 2015 and 2025 that were selected through identification, screening, and eligibility evaluation processes. The findings indicate a shift from conventional design approaches toward integration based on Building Information Modeling (BIM), design automation, network optimization, and the application of IoT and edge computing technologies. Structured cabling is identified as the core infrastructure supporting subsystem integration, while data security, bandwidth management, and integrated fire monitoring systems are critical to ensuring building reliability. These findings highlight the importance of a BIM-based integrative approach and digital documentation from the early planning stage to support standard compliance verification and the development of measurable technical indicator-based evaluation models.

The increasing demand for clean energy and the urgency of achieving Indonesia’s Nationally Determined Contribution (NDC) targets and the 2060 Net Zero Emission commitment highlight the need for reliable renewable-based power systems to support emerging green hydrogen development. Hydrogen is recognized as a strategic energy carrier for decarbonizing hard-to-abate industrial sectors, thereby increasing the importance of stable renewable electricity supply for electrolysis processes. This study aims to design and analyze a Solar Photovoltaic (PV) system integrated with a Battery Energy Storage System (BESS) as the primary electricity source for the hydrogen electrolysis process at PT. PLN Nusantara Power Unit Pembangkitan Gresik. A quantitative approach was employed through load assessment, technical modeling of the PV and storage systems, and simulation using PVsyst software. The results indicate that a 1,252 kWp PV system can deliver an annual energy output of 1,362 MWh, achieving a specific yield of 1,088 kWh/kWp/year and a Performance Ratio of 56.93%. This production closely matches the annual energy demand of the hydrogen plant, which reaches 1,414.2 MWh. Consequently, the integration of a BESS is essential to maintain supply continuity during periods of reduced solar irradiance and to stabilize power input to the electrolyzer. The findings demonstrate that the proposed PV–BESS configuration significantly increases the contribution of renewable energy, reduces dependency on the grid, and supports the sustained production of low-carbon hydrogen. Practically, this study provides a scalable technical model for integrating solar and storage systems to accelerate green hydrogen deployment in Indonesia, particularly within industrial power generation facilities.

Transformasi sistem tenaga listrik menuju konsep smart grid menuntut kemampuan prediksi beban listrik yang akurat, adaptif, dan andal guna mendukung pengelolaan energi secara real-time. Seiring meningkatnya kompleksitas sistem akibat integrasi energi terbarukan, perangkat Internet of Things (IoT), serta dinamika konsumsi energi, pendekatan berbasis machine learning menjadi solusi yang semakin banyak diadopsi dibandingkan metode statistik konvensional. Penelitian ini bertujuan untuk memetakan perkembangan, mengklasifikasikan metode, serta menganalisis performa penerapan machine learning dalam prediksi beban listrik pada sistem smart grid melalui pendekatan Systematic Literature Review (SLR). Metode penelitian mengacu pada pedoman PRISMA 2020 dengan tahapan perumusan research question, strategi pencarian literatur, penerapan kriteria inklusi dan eksklusi, serta proses ekstraksi dan sintesis data. Pencarian dilakukan pada basis data ilmiah terindeks dengan rentang publikasi 2016–2025. Berdasarkan proses seleksi, diperoleh sembilan artikel yang memenuhi kriteria dan dianalisis menggunakan pendekatan deskriptif dan komparatif untuk mengidentifikasi tren metodologi dan perbandingan performa. Hasil kajian menunjukkan bahwa metode Long Short-Term Memory (LSTM) merupakan algoritma yang paling dominan digunakan karena kemampuannya memodelkan data deret waktu dan pola nonlinier. Tren terbaru mengarah pada model hibrida dan arsitektur transformer yang menawarkan peningkatan akurasi dan stabilitas. Evaluasi performa umumnya menggunakan metrik MAE, RMSE, dan MAPE, dengan pendekatan deep learning menunjukkan hasil lebih unggul dibandingkan metode tradisional. Tantangan utama meliputi kualitas data, kompleksitas komputasi, generalisasi antarwilayah, dan interpretabilitas model. Penelitian selanjutnya direkomendasikan untuk mengembangkan model yang lebih efisien, adaptif, dan terintegrasi dengan sistem manajemen energi berbasis IoT serta real-time analytics. The transformation of electric power systems toward the smart grid concept requires accurate, adaptive, and reliable load forecasting capabilities to support real-time energy management. As system complexity increases due to the integration of renewable energy, Internet of Things (IoT) devices, and dynamic energy consumption patterns, machine learning-based approaches are increasingly adopted compared to conventional statistical methods. This study aims to map developments, classify methods, and analyze the performance of machine learning applications in electric load forecasting within smart grid systems through a Systematic Literature Review (SLR) approach. The research method follows the PRISMA 2020 guidelines, including the formulation of research questions, literature search strategy, inclusion and exclusion criteria, and data extraction and synthesis processes. The literature search was conducted across indexed scientific databases covering publications from 2016 to 2025. Based on the selection process, nine articles met the criteria and were analyzed using descriptive and comparative approaches to identify methodological trends and performance comparisons. The findings indicate that Long Short-Term Memory (LSTM) is the most dominant algorithm due to its capability to model time-series data and nonlinear patterns. Recent trends point toward hybrid models and transformer architectures that offer improved accuracy and stability. Performance evaluation commonly employs MAE, RMSE, and MAPE metrics, with deep learning approaches generally outperforming traditional methods. Key challenges include data quality, computational complexity, cross-regional generalization, and model interpretability. Future research is recommended to develop more efficient, adaptive models integrated with IoT-based energy management systems and real-time analytics.